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Record W4301356004 · doi:10.46692/9781447334927.004

The “lumpiness” thesis revisited: the venues of policy work and the distribution of analytical techniques in Canada

2018· other· en· W4301356004 on OpenAlexaffabout
Michael Howlett, Seck Tan, Adam Wellstead, Andrea Migone, Bryan Evans

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWork (physics)Distribution (mathematics)EconomicsEconometricsBusinessLabour economicsEngineeringMathematicsMechanical engineeringMathematical analysis

Abstract

fetched live from OpenAlex

Introduction: analytical techniques and policy analysis At its heart, policy analysis is what Gill and Saunders (1992, pp. 6–7) characterized as “a method for structuring information and providing opportunities for the development of alternative choices for the policymaker.” This involves providing information or advice to policy makers concerning the relative advantages and disadvantages of different policy choices (Mushkin, 1977; Wildavsky, 1979). Professional policy analysts employ many different types of tools in this work (Mayer et al., 2004; Colebatch et al., 2011). These tools are generally designed to help evaluate current or past practices and aid decision-making by clarifying or eliminating many possible alternative courses of action. In this sense, these policy tools play a significant role in policy formulation activity and potentially play a significant role in determining the content of policy outputs and thus policy outcomes (Sidney, 2007). As such they are a worthy subject of investigation in their own right. Unfortunately, however, generally speaking little is known about many of the practices involved in policy work (Colebatch, 2005; Colebatch, 2006; Colebatch & Radin, 2006; Noordegraaf, 2011) nor about the tasks and activities involved in policy formulation (DeLeon, 1992; Linder & Peters, 1990). That is, although many works have made recommendations and suggestions for how formulation should be conducted (Vining & Weimer, 2010; Dunn, 2004), very few works have studied how it is actually practised on the ground, and data is limited on virtually every aspect of the policy appraisal activities in which governments engage (Page, 2010; Page & Jenkins, 2005). Some progress has been made on this front in recent years. Nilsson, Jordan, Turnpenny and their colleagues have made considerable progress in, for example, mapping many of the activities involved in both ex post and ex ante policy evaluation (Nilsson et al., 2008; Hertin et al., 2009; Turnpenny et al., 2009). This has been joined by work done in Australia and elsewhere on regulatory impact assessments and other similar tools and techniques used in formulation activities (Carroll & Kellow, 2011; Rissi & Sager, 2013). In addition, more evidence has slowly been gathered in these countries and elsewhere on the nature of policy work and the different types practised in different situations by different actors (Mayer et al., 2004; Boston et al., 1996; Tiernan, 2011; Sullivan, 2011).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.290
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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