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Record W4398369295 · doi:10.7910/dvn/ktwodg

Social Work Doctoral Scholarship in Canada

2013· dataset· en· W4398369295 on OpenAlexaffabout
David W. Rothwell, Lucyna Lach, Anne Blumenthal

Bibliographic record

VenueHarvard Dataverse · 2013
Typedataset
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsScholarshipWork (physics)SociologySocial workPolitical scienceEngineeringMechanical engineeringLaw

Abstract

fetched live from OpenAlex

The nature and types of social work research generated in doctoral programs has been well-studied in the US. Doctoral programs in Canada, in comparison, are much younger; the first doctoral program was created in the mid-1970s. To date, there is no systematic understanding of the nature and types of knowledge produced within Schools and Faculties of Social Work. In this study we review 248 dissertations that were published in Canada between 2001 and 2011. Method: A database was created from a search of Canadian social work dissertations published between 2001-2011 using the ProQuest Dissertations and Theses Database (PQDT). To verify the sample, doctoral program directors were contacted and independently confirmed the sample names and dissertation titles by year. Full text dissertations were retrieved for review. Data pertaining to 12 research methodology variables (e.g., quantitative/qualitative/mixed method, presence/absence of hypothesis, primary or secondary data source, sample size, etc., see codebook) was extracted from each dissertation. Inter-rater reliability for a random selection of 1/3 of the cases was 0.82. Conflicts w ere resolved through consensus.

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)0.007
metaresearch head score (Gemma)0.035
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.914
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0280.071
Science and technology studies0.0090.002
Scholarly communication0.0090.002
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0250.006

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 head0.066
GPT teacher head0.340
Teacher spread0.275 · 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
GenreDataset

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

Citations1
Published2013
Admission routes2
Has abstractyes

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