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Record W7115585571 · doi:10.82396/cjcd.v14i1.3082

From Hero to Zero

2021· article· en· W7115585571 on OpenAlexaff

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsIdentity (music)Coping (psychology)HEROService (business)Identity formation

Abstract

fetched live from OpenAlex

This article discusses the identity processes involved in the transition to retirement for emergency service workers, which includes police officers, fire fighters, and emergency medical personnel. The author conducted a study using the Enhanced Critical Incident Technique (ECIT) to determine what helped, hindered and might have helped emergency service workers in making decisions regarding retirement. One of the factors participants identified as hindering their ability to retire was the loss of their professional identity. This loss was discussed and compared to theories that explain the processes of identity role assimilation and identity role salience. Theory and research regarding the accumulation, maintenance and narrowing of the number of identity roles is discussed. The tendency of emergency service workers to narrow their identity role to the singular professional role is examined for its impact on coping and for its subsequent impact on retirement. Future directions in research and practice are suggested.

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)0.001
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.007
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0270.008

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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