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Record W2808131260 · doi:10.1080/14427591.2018.1480409

Applying case study methodology to occupational science research

2018· article· en· W2808131260 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Occupational Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)Process (computing)Occupational scienceCase study researchManagement scienceResearch methodologyComputer scienceData scienceKnowledge managementPsychologySociologyEngineeringOccupational therapyGeography

Abstract

fetched live from OpenAlex

Case study methodology offers a creative and flexible way to gain a comprehensive understanding of human complexities in context, using various means to collect data. This paper is divided into two parts. Part one provides a brief overview of what case study methodology is; and part two presents an integrated review (Whittemore & Knafl, 2005) on how case study has been used for the study of occupation. Findings indicate that while case study methodology is increasingly used for the study of occupation, many of its essential features are absent in published research, such as a definition of the bounded case in its context, use of multiple sources of data, and detailed information about the research process in the output. Recommendations are provided on these essential features of case study to advance the effective use of case study methodology for studying occupation.

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.

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)0.070
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0700.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0080.002
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.785
GPT teacher head0.733
Teacher spread0.052 · 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