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Record W4390063627 · doi:10.3138/cjpe.75803

Contribution Analysis of a Complex System During Disruptions

2023· article· en· W4390063627 on OpenAlexvenueno aff
Sharon L. Newbill

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachOccupational safety and healthAgricultureBusinessCoronavirus disease 2019 (COVID-19)Environmental planningEnvironmental resource managementPolitical scienceEnvironmental healthGeographyEnvironmental scienceMedicine

Abstract

fetched live from OpenAlex

The U.S. National Institute of Occupational Safety and Health (NIOSH) supports 11 regional centers to conduct research and outreach in agriculture, fishing, and forestry. The Southwest Center for Agricultural Health, Injury Prevention, and Education serves Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. The 2016–2022 evaluation used contribution analysis since it is effective in evaluating complex systems with diverse stakeholders and multiple projects, with partners working toward the same outcome over time. The evaluation responded to the disruptions in program implementation caused by the COVID-19 pandemic and to NIOSH modifications to key elements of contribution analysis procedures.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.438
GPT teacher head0.555
Teacher spread0.117 · 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.

Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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