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Decision-maker-friendly research

 

Péter Inkei

Decision-maker-friendly research in culture

Taking a look at research in culture, one soon arrives at the familiar feeling: too much and too little at the same time. Academia has been producing great number of research articles, dissertations, conference papers, project reports etc. on cultural subjects, including on cultural policies. Yet seldom can we come across cases when decisions taken in the field of cultural policies are directly linked to research. Lack of will and lack of appropriate research both are behind too little use.  

By stating “too little”, I confirm that more would be better and desirable. I consider it an axiom that cultural policy decisions can be better with more use of research.

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Who are the actors in this game? How can they improve the situation, what leads to more use of research in culture? More in the sense of amount, and in effectiveness.

o    Decision makers: politicians and senior officers on various levels, from the European Commission down to small towns. Members of elected bodies like parliaments, self-governments. 

o    Researchers.

o    Third parties.

·         Staff at decision-making administration.

·         Observatories or organisations with such functions.

·         Media.

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Decision makers

Curiousity and openness to alternatives are key traits.

These properties imply the need to communicate with researchers. Face to face, whenever possible; with a broader selection of people beyond the established gurus. That helps in asking the right questions.

Also, when researchers have reported, decision-makers should ask back again, before deciding. Such communication is helpful for both sides. Conclusions in social sciences almost always lead to multiple interpretations – politicians’ checkback and feedback makes researchers clarify, specify or modify their statements, and leads to guiding the reader towards more relevant conclusions and decisions. 

Such communication can result in contemplated restraint from ordering new research, before accumulated existing research knowledge is thoroughly mapped. (Don’t reinvent the wheel.) Findings of great many projects, surveys lie there waiting for fuller exploitation. The same is true for statistical collections. Also, comparative studies to learn how an issue is handled abroad or in other villages, towns, regions.

Another form of the manifestation of decision maker’s patience is not to expect too much, too soon and too straight. Even the most direct research advice needs further thinking and deliberation: work to do by the decision-maker. The familiar intelligence string expresses it like this: data informationknowledgewisdom, where the latter is expected from the decision-maker. To which I would add identification of problem at the beginning, and vision or inspiration at the end: wisdom means being aware of the scale of options (where one alternative is always “don’t change, don’t do anything”). But wisdom sometimes prevents from taking the decision that proves to be the right one on long term. Inspiration tells a successful politican from the mediocre one. The full scale therefore reads: problem – data informationknowledgewisdom – inspiration.

It is unavoidable that decision-makers use science to legitimise their decisions. In a way, we are advocating exactly for this: to convince politicians that their decisions are more legitimate if these are substantiated by scientific evidence. Owing to the complex nature of social phenomena scientific evidence nevertheless allows for a great variety of conclusions, often opposite ones. Politicians and decision-makers – just like most ordinary readers – search for those that support their intentions, plans, values and beliefs. A rich choice of research conclusions from which to select is better than no research, even if much of the selection is biased.

The problem arises when biased selection of scientific arguments becomes the main or exclusive function of science, when truth is secondary to legitimation. When apologetic “policy-based evidence” replaces evidence-based policies.

Very general is when citing research is a smokescreen for lack of vision. Using concepts concocted by researchers (e.g. creative sector, conflict prevention by arts etc.) without translating their content into really relevant decisions is such symptom.

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Researchers

In the context of the use of science in cultural policy decisions researchers’ self-restraint about their own research agenda singles out. Instead, high degree of empathy towards the user is the basic requirement, particularly in the rendering of the findings.

The main temptation to withstand is to impress too much. Scholars are constantly coerced to impress: first their professors and dissertation boards, then university or other superiors, peers in the profession, grant giving juries etc. and often quantity is the first resort. Short is not serious enough.

True, decision-makers also tend to measure the value of research advice by the size. Professional consultants are masters in inflating their reports to hundreds of pages.

We need not change the world and prohibit length. It may serve the comfort feeling of both the researcher and the decision maker. But we should acknowledge that the essence rarely needs more than a couple of pages. Sometimes a couple of paragraphs. This underscores the significance of the executive summary.

Often executive summaries are also subject to the same disease of obesity. They also try to tell everything instead of focusing on the absolutely essential and imperative. Will the art of executive summary give way to the art of abstracting the executive summaries?

In my eyes the main virtue is brevity and focus.

A table like the following is alright in a research paper. (Taken from a Eurostat database.) Disfunctional if part of a report for the intention of decision-makers.

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Germany

93 598 619

95 212 372

96 826 125

98 439 878

100 053 631

101 667 384

103 281 137

105 907 463

103 434 669

107 776 798

United Kingdom

65 745 970

65 525 616

65 305 262

65 084 908

64 864 554

64 644 200

64 423 846

62 577 674

67 402 044

57 735 885

Spain

51 341 164

52 422 350

53 503 536

54 584 722

55 665 908

56 747 094

57 828 280

56 429 185

54 470 376

55 617 853

Italy

44 718 627

46 145 016

47 571 405

48 997 794

50 424 183

51 850 572

53 276 961

53 749 362

54 375 079

55 019 507

Netherlands

10 327 200

11 814 700

13 302 200

14 789 700

16 277 200

17 764 700

19 252 200

18 997 700

19 039 000

19 125 200

Sweden

15 104 872

15 510 014

15 915 155

16 320 297

16 725 438

16 917 687

17 961 507

17 727 970

17 949 438

18 480 781

Poland

9 600 637

10 383 610

11 166 583

11 949 557

12 732 530

13 198 537

14 559 756

15 509 790

15 491 770

16 326 526

Austria

7 229 379

7 585 832

7 942 285

8 298 739

8 655 192

8 946 111

9 450 445

9 756 462

10 032 283

10 490 006

Finland

7 039 231

7 119 178

7 199 125

7 279 072

7 359 019

7 317 909

7 626 946

7 745 964

7 544 726

7 885 936

Portugal

5 285 792

5 589 576

5 893 361

6 197 145

6 500 930

6 697 384

7 256 364

7 438 542

7 649 273

7 746 207

Greece

4 134 716

4 581 423

5 028 130

5 474 837

5 921 544

6 233 813

7 083 835

7 127 227

7 523 582

7 044 471

Czech Republic

6 561 310

6 509 822

6 458 334

6 406 846

6 355 358

6 289 452

6 281 217

6 186 476

5 953 539

5 877 882

Romania

2 835 867

3 247 477

3 659 087

4 070 697

4 482 307

4 836 196

5 420 968

5 659 416

4 865 545

4 726 414

Hungary

3 223 118

3 354 329

3 485 541

3 616 752

3 747 964

3 872 797

4 023 143

4 135 220

3 922 670

3 918 201

Denmark

3 006 595

3 165 752

3 324 909

3 484 066

3 643 223

3 754 900

4 056 498

4 073 214

3 794 578

3 852 951

Slovakia

1 112 041

1 277 681

1 443 322

1 608 962

1 774 603

1 956 050

2 074 270

2 287 331

2 058 218

2 042 109

Croatia

1 345 993

1 382 883

1 419 772

1 456 662

1 493 551

1 517 902

1 592 407

1 591 681

1 367 019

1 266 897

Slovenia

240 413

356 560

472 708

588 855

705 003

839 214

901 170

1 071 509

1 104 160

1 083 539

Estonia

574 387

630 798

687 208

743 619

800 029

831 504

962 721

944 325

766 593

837 811

Cyprus

474 678

484 046

493 414

502 782

512 150

516 525

540 873

535 261

597 159

614 907

Lithuania

115 327

120 024

245 188

370 352

495 516

620 680

745 844

733 112

536 993

569 601

Iceland

327 312

353 292

379 272

405 252

431 232

457 212

483 192

494 999

538 333

537 659

Latvia

296 171

344 998

393 824

442 651

491 477

513 593

642 551

611 246

360 023

433 764

Malta

95 382

103 436

111 490

119 544

127 598

135 652

143 706

142 289

158 876

158 387

Liechtenstein

1 784

2 281

2 778

3 275

3 772

4 349

4 767

5 021

5 921

5 432

 

 

 

For decision-makers an extract works better:   

 

2001

2005

2009

2010

Portugal

5.2

6.5

7.6

7.7

Greece

4.1

5.9

7.5

7.0

Romania

2.8

4.4

4.8

4.7

Slovakia

1.1

1.7

2.0

2.0

Croatia

1.3

1.4

1.3

1.2

Slovenia

0.2

0.7

1.1

1.0

Estonia

0.5

0.8

0.7

0.8

Cyprus

0.4

0.5

0.5

0.6

Lithuania

0.4

0.4

0.5

0.5

Iceland

0.3

0.4

0.5

0.5

Latvia

0.2

0.4

0.3

0.4

Malta

0.09

0.1

0.1

0.1

Liechtenstein

0.001

0.003

0.003

0.005

 

Especially if combined with a schematic reference to the fuller scene:

Professionals know how to avoid the trap of undigestible numbers maintaining at the same time confidence in the comprehensive nature of the statistical source.

Researchers face particular challenges with regard to assessing sustainability, impact, side- and countereffects of measures. Since this is a difficult terrain indeed, researchers tend to avoid it – not to speak of decision-makers.

Researchers – not necessarily each individual but as a community – should avoid favouring one genre at the expense of the other: overemphasising empirical and neglecting theoretical studies or the other way round. Also wisdom is required with regard to the status of pure sciences. Policy makers tend to be shocked by the wide array of research topics at universities of conferences which do not seem to offer her/him any clue to their daily dilemmas. It is essential to prove that without those seemingly superfluous  research science is not able to arrive at substantial policy recommendations. Solidarity and common sense requires that the link between the two should be demonstrated. Decision-makers must not be left in the illusion that only what is done at her/his request matters, the rest is waste of money for researchers’ self-fulfilment.

Finally and similarly, researchers should act for soothing the opposition between the bogeys of instrumentalism of culture on the one hand and  intrinsic culture on the other (culture for culture’s sake). Politicians and dogmatically inclined persons tend to demonise one or the other. We will be better off by accepting that culture is like almost all human pursuits. They serve other aims in the life of the individual or the society, and have their rewards in themselves. Like work, or learning, or sports, or eating. Or sex.

***

With the example above we have entered the realm of statistics. A very week point for culture in the European Union. In the EU harmonisation, standardisation and consolidation of statistical information has reached high levels of perfection and sophistication – elsewhere, not in culture. Here such simple statements as the percentage of national budget spent on culture have 27 interpretations, with sometimes considerable differences between them. Simple notions like theatre attendance, or number of translated titles still require precision. Eurostat does not collect cultural data of its own. Collections are sporadic and consolidation is far from perfect. Attempts like ESSNET arrive at recommendations only.

Eurobarometer surveys are criticised on the ground that they lack the totality that national regular statistical collections and surveys have – nevertheless Eurobarometer offers the only regular surveys applying the same methodology.

The fundamental problem with statistics is that they are by nature redundant. In papers meant for decision-makers, researchers must turn maximum care on selection and focus. They must search for indicators that merit the name: indicators that really indicate something more than themselves. A cultural statistics item may have little sense in itself. “The number of visitors grew / fell by 1.5%.” That may have significance for the management, for the funder or the superficial journalist. Cultural administrators (politicians), however, need to know the context, the deeper meening of the indicator: what does the 1.5% drop / growth mean for the community? For the broader cultural, social or political agenda?

Therefore seeking or creating statistical indicators that indicate best is a vital role of the researcher. Indicate in two senses: explaining phenomena with which there is some causal relationship (“attendance fell / grew because…”), or signalling trends or phenomena. (“Where / when this happens, there usually signals that and that.” E.g. cinema attendance…, watching tv programmes, subscribing to specific newspapers.)  

This cryptic example also points at the importance of benchmarking. The 1.5% flop / rise can be appreciated by knowing previous data, or comparative data from elsewhere. 

Statistics by definition denote what exists. For cultural policies, however, the void, the deficits are of particular importance. Therefore research, surveys and statistical collections must search for indicators that best show where are tasks to do. The share and compositions of citizens not reached; facilities that are underused etc.

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Third parties

Staff

The role of staff at administrations is vital in communicating needs towards research, and interpreting findings. Whenever we used decision-makers in this text, it was done with the implication that they are totally helpless without able staff members around.

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Observatories

The term observatory is used metaphorically here. What I have in mind institutions with functions that are eminently embodied in some cultural observatories in Europe. The essence of which is acting as an interface between academically motivated research and pragmatic applications of intelligence.

Many observatories do much more. They collect and process data, monitor phenomena, run research projects, do training etc. It is the transmission between the administrations and the world of academia is what counts most in this context.

Of course university departments, research institutes, specilalised ngos and consultancies can play the role of interface, too.

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Media

Articles, reports, investigative studies done for the media often have properties that are best needed for practical link between scientific research and political decisions. Limited time and space obliges the authors to be brief, selective and to the point, focusing on the important and the interesting: what catches attention, surprises, shocks or cries for change. Illustrations also serve the same goal.

These are the very features that create suspicion and reservation towards science as communicated trough the media. Superficial, simplistic, misleading – goes the condemnation. The criticism is right, as it indeed touches on basic properties of science in media. Nevertheless this is the price to pay. Grabbing large attention, making impact requires condensation, simplification, strong effects. If they are based on solid evidence, their use is alright. Problems arise – and indeed, they often do – if the hunt for sensation overwrites the respect for facts, truth and honesty.

Which points at the community of researchers again.

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Conclusions

In order to achieve more effective use of research in cultural life and policies, structural improvements and changes in attitude are both required.

To the first, structural improvements, belong the accelerated improvement of the consolidated statistical services in the EU, the development of institutional conditions of interfacing research and decision making (the observatory model), and the acknowledgement of the role of responsible quality media.

As to attitudes that favour effective co-operation, there are lessons for both two main parties, decision-makers and researchers. The one point to be emphasised again on the part of the decision-makers is the desire to communicate more with researchers in direct. The success of science in supporting decision-making seems to depend more on the latter, on the researchers. When preparing their reports, researchers must identify themselves with the particularities of the situation that the decision-maker is typically in.