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Record W2015255563 · doi:10.3138/cjpe.29.1.1

Evaluating the Identity of Program Recipients Using an Identity Exploration Instrument

2014· article· en· W2015255563 on OpenAlexaffvenue
Elaine Hogard

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsNOSM University
Fundersnot available
KeywordsIdentity (music)Identification (biology)Strengths and weaknessesPsychologyProcess (computing)Social psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract: This article argues that the self and identity of program recipients should be an important variable in program evaluations. In a smoking cessation program, for example, the aim should be to achieve a change in the way recipients view themselves and their identity as smokers or nonsmokers. The article identifies a gap in published studies that consider self and identity as an outcome or process measure in program evaluation. The potential of such an approach to give added depth to program evaluation is considered. Three studies in this area are identified and summarized: the identity of parents after child death; the professional identity of students after a program of interprofessional education; and the characteristics of male identity in Germany. To identify possible approaches, conceptualizations of self and identity and methods of exploring and measuring it are considered, culminating in the identification and description of a synthetic theory of identity, Identity Structure Analysis (ISA), and its associated measuring tool, Ipseus. The choice of this method as part of a program evaluation is justified. The use of ISA/Ipseus in three program evaluations—decision-making in community safety; student constructions of theory and practice in nurse education; and demands and tensions in nursing lecturing—is described. The strengths and weaknesses of this approach are considered.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.391
GPT teacher head0.523
Teacher spread0.132 · 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 designOther design
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

Citations9
Published2014
Admission routes2
Has abstractyes

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