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Record W7034896353

Using a critical occupational perspective to locate – and begin to fill – “cracks” in public policy

2016· article· en· W7034896353 on OpenAlexaboutno aff

Bibliographic record

VenueCommonKnowledge Research Repository (Pacific University Oregon) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrthoptera Research and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Public policyGovernment (linguistics)ScarcityWork (physics)Agency (philosophy)Order (exchange)Cognitive reframing
DOInot available

Abstract

fetched live from OpenAlex

Topic: Public policies in North America are constructed according to a market view of society wherein individuals are reduced to classifications or definitions that can be easily grouped and governed (Stone, 2012). In these policies, there is an increasing emphasis on citizens’ moral obligations to achieve self-sufficiency through contributions to the market (Schram et al., 2010). The economics-based approach to policy trades holism for categorization and equates work with societal participation, fostering exclusion when people’s situations do not fit neatly within these pre-defined boxes. Attending to public policy requires complicating its application and understanding how policy mandates are negotiated and achieved. If occupational scientists aim to shape public policy, they must grapple with the contributions that a holistic occupational perspective can make within the market-based policy arena, as well as the potential impacts of scholarship that examines the implications of policy for service provision and everyday life.\nPurpose: The purpose of this paper is to illustrate how the occupational perspective can expose, explain, and begin to fill “cracks” in public policies that purport to support citizens’ everyday lives and societal participation. A pair of presenters from the United States and Canada will present their research about unemployment to highlight the contributions that an occupational lens can make to various policy discussions.\nMethods: The presenters will discuss the elements of their multi-sited research, including the multiple perspectives within the policy arena that they are trying to understand through collaborative ethnography (Lassiter, 2005) and situational analysis (Clarke, Friese, & Washburn, 2015).\nIntent: The presenters will identify how their research a) addresses specific public policy issues, b) generates knowledge about how policies are “made” through front-line service provision (Lipsky, 1980/2010), c) illustrates the complexities of occupation that are obscured in market-based policy approaches, and d) demonstrates that a focus on everyday occupation illuminates the supports and tensions that issue from policy mandates. Attendees will gain insights into the potential policy contributions that stem from critical occupational science research. Based on these insights, attendees will have a foundation for identifying other social needs and policy initiatives that can be critiqued and enhanced through occupational science research.\nImportance to occupational science: This presentation will generate concrete ideas for analyzing and influencing public policy from an occupational perspective. It is important for occupational scientists to understand how public policy can be a vehicle for impacting occupational engagement at community and societal levels.\nObjectives for discussion period: What kinds of data are useful to service providers and policy makers? What is the cost of neglecting occupational needs in public policies? What forms of knowledge mobilization can be used to transform service provision and influence public policies?

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0280.089
Scholarly communication0.0240.028
Open science0.0030.013
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0060.001

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 head0.182
GPT teacher head0.353
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2016
Admission routes1
Has abstractyes

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