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Record W1731692312 · doi:10.3138/cjpe.023.010

Professional Identity of Evaluators in Israel

2008· article· en· W1731692312 on OpenAlexvenueno aff
Miri Levin‐Rozalis, Efrat Shochot-Reich

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Identity (music)Context (archaeology)Public relationsProfessional associationProfessional developmentPolitical sciencePublicationSociologyPedagogyLawGeography

Abstract

fetched live from OpenAlex

Abstract: Professional identity is a precondition for the establishment of a profession. This examines the professional identity of program evaluators in Israel. The field of evaluation in Israel has developed differently than in most Western countries—from the ground and with minimal governmental interference—and thus it is an interesting case study. In spite of the diversity of the backgrounds of evaluators, there is strong agreement among them on the core of evaluation as an interdisciplinary profession whose aim is mainly as an advisory tool that serves for learning. They also strongly agree that the borders and essence of evaluation are not clear to evaluators, evaluees, and the public. While half of the respondents practicing evaluation do not identify themselves as evaluators, a professional community is important to them. Evaluators in Israel are not well connected to professional activities and developments outside of the country. They do not participate in international conferences and do not publish in scientific journals, yet they are very active in professional activities in Israel. The context of Israeli society is analyzed for a better understanding of these findings.

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)0.015
metaresearch head score (Gemma)0.026
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.004
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.517
GPT teacher head0.591
Teacher spread0.075 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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