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Record W7096897469

1 Becoming Social Science Researchers: Learning and Enacting New Practices and Identities

2015· article· en· W7096897469 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)ProductivityGraduate studentsSpace (punctuation)Higher education
DOInot available

Abstract

fetched live from OpenAlex

In recent years, reports have been mounting that advocate the need for university faculty renewal and expanded research capacity across jurisdictions. Elliott (2000) projected that between 25,000 and 30,000 new faculty members will be required by 2010 to replace retiring faculty members, accommodate increasing student enrolments, and expand research capacity within Canada. Reports further suggest that there are insufficient numbers of Ph.D. students and recent graduates to take up these shortfalls (Castle & Arends, 2000; Smith, 2000). Comparable concerns have also been raised in the US, the UK, and elsewhere (e.g., Marx, 2002; Recruitment and retention of staff, 2002; West, 2001). Rae (2005) argued that Ontario must double its graduate enrollment over the next decade to replace retiring faculty members, increase research productivity to compete globally, and provide space for double-cohort students who will soon complete undergraduate degrees (i.e., the extra influx of students following the phase out of the Grade 13 or OAC year in Ontario). Provincially, nationally, and internationally, the message seems consistent: more faculty members are required and therefore more graduate students are needed. However, sheer numbers are neither the only issue nor the only solution to research capacity building. Graduate programs and faculty ranks must be opened to diverse scholars, where diversity is defined broadly to include considerations about race,

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.802
GPT teacher head0.698
Teacher spread0.104 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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