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Record W2036342338 · doi:10.1177/1356389003009002006

Reducing Anxiety and Resistance in Policy and Programme Evaluations

2003· article· en· W2036342338 on OpenAlexaff
Iris Geva‐May, Warren Thorngate

Bibliographic record

VenueEvaluation · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsCarleton UniversitySimon Fraser University
Fundersnot available
KeywordsResistance (ecology)AnxietyProcess (computing)PsychologyPolitical sciencePublic relationsSocial psychologyComputer scienceEcologyBiology

Abstract

fetched live from OpenAlex

Evaluators of an organization whose programmes or policies are under evaluation frequently encounter anxiety and resistance from the evaluees. Literature abounds with suggestions for developing collaborative interactions. Few studies, if any, show circumstances where the evaluees' need for co-operation and support transcends the inherent anxiety and resistance and triggers for the evaluees' desire for moral support and advice. An analysis of the socio-psychological factors involved in the circumstances described in this study may shed light on how the evaluator can develop the evaluation process to enhance the collaboration with the evaluees. Here we report and analyse three case histories of such evaluations to determine the critical features of the evaluations that made them collaborative rather than conflictual. The common issues raised shed light on practices that alleviate anxiety in the evaluation process.

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)0.087
metaresearch head score (Gemma)0.280
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.280
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.007
Scholarly communication0.0110.005
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.227
GPT teacher head0.534
Teacher spread0.307 · 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 designQualitative
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

Citations15
Published2003
Admission routes1
Has abstractyes

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