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Record W2937389648 · doi:10.55016/ojs/jet.v1i2.43491

Studentship and Membership: A Study of Roles in Learning

2018· article· en· W2937389648 on OpenAlexaffabout
Alan M. Thomas

Bibliographic record

VenueJournal of educational thought. · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsCanadian Association for the Study of Adult Education
Fundersnot available
KeywordsProsperityArgument (complex analysis)PoliticsProductivitySociologyPositive economicsPolitical economyPublic relationsPolitical scienceSocial psychologyPsychologyEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

It is a curious fact that even the most radical political approaches to education as a social issue of growing prominence still attach their arguments and nostrums to the education of children and youth. It has not yet become apparent in many of the centres of public debate and analysis that the educational system can never again be considered a matter of concentration on the young. That this is true is not simply because of the contemporary awareness of governments of the close relationship of productivity and prosperity to trained and retrained manpower, nor because of the recently resulting availability of substantial financial support for an enterprise of this kind. Actually, the latter situation has prompted voices within organizations like the Canadian Teachers' Federation to argue that an imbalance in support has already occurred, and that elementary education, where all begins, is being slighted in favour of secondary and post-secondary.Rather the argument that we can no longer concentrate only on the young is based on two more fundamental factors that seem at the moment at any rate to have the right to be treated as facts.

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)0.010
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0140.024
Scholarly communication0.0150.013
Open science0.0030.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.026
GPT teacher head0.401
Teacher spread0.375 · 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 designObservational
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
Published2018
Admission routes2
Has abstractyes

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