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Record W3111795863 · doi:10.62707/aishej.v12i3.491

COVID-19 ‘Targets’ the National Access Plan

2020· article· en· W3111795863 on OpenAlexaff
Linda Cardiff, Michele Kehoe

Bibliographic record

VenueAISHE-J · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsTrinity College
Fundersnot available
KeywordsPlan (archaeology)Coronavirus disease 2019 (COVID-19)Context (archaeology)PandemicEquity (law)NapPublic relationsDialog boxPolitical scienceMedical educationPsychologyMedicineComputer scienceGeographySocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract. This reflective piece examines the articulation of the vision presented in the National Plan for Equity of Access to Higher Education (NAP) in the context of the educational challenges faced by the target groups arising from the Covid-19 emergency. In addition, the piece aims to identify those who are outside the current NAP and make recommendations to address issues identified. For many of those identified as part of the plan, their educational experience changed overnight and brought with it both challenges and opportunities. As the pandemic impacted the lives of all it became apparent that others who were outside the NAP should be given a chance for their voices to be heard. The next plan needs to reflect the immediate and longer-term impact of the changes that have been experienced in education and give voice to a wider target group.

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)0.012
metaresearch head score (Gemma)0.023
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0060.004
Open science0.0020.008
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0240.005

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 head0.267
GPT teacher head0.529
Teacher spread0.262 · 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
GenreCommentary

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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