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Record W6969614934 · doi:10.5683/sp3/gu8s4q

Survey of Earned Doctorates, 2003-2008 [Canada] [Excel]

2015· dataset· en· W6969614934 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2015
Typedataset
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsReceiptHigher educationDebtCensusStudent debtCareer pathAffect (linguistics)Parliament

Abstract

fetched live from OpenAlex

<p>The Survey of Earned Doctorates (SED) is an annual census of doctorate recipients in Canada that was conducted for the first time on a national basis during the 2003-2004 academic year. The basic purpose of this survey is to gather data about all doctoral graduates in Canada to inform government, associations, universities and other stakeholders on the characteristics and plans of these very highly qualified graduates as they leave their doctoral programs.</p> <p>The survey's key data objectives are to evaluate the impact of the various sources of institutional funding; to gather information on the retention of doctoral students in Canada; to gain a better understanding of postgraduate education financing and debt level; to allow labour market planners to assess the additions to the domestic stock of highly qualified human resources in various fields; and, to allow an examination of the path to receipt of doctoral degrees and the impact of foreign students. </p> <p>The survey collects data about the graduate's postsecondary academic path, funding sources, field of study and his/her immediate postgraduate plans.</p> <p>The data from the SED can be used by universities and governments to make policy decisions that affect graduate education throughout Canada, by federal agencies to inform parliament and to make decisions about financial commitments that affect graduate education throughout Canada; and, in the evaluation of graduate education programs, strategic planning at the provincial level, labour force projections, and affirmative action plans at all levels. </p>

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.293
Teacher spread0.252 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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