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Record W2625145328

Essential Skills Computerized Occupational Readiness Training (ESCORT) Demonstration Project, Canada.

2006· book-chapter· en· W2625145328 on OpenAlexaboutno aff
Patrick J. Fahy, Patrick F. Cummins

Bibliographic record

VenueAUSpace (Athabasca University) · 2006
Typebook-chapter
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationTraining (meteorology)Engineering managementEngineeringMedicineGeography
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT: This chapter describes the purpose, processes, and effects of an e-learning employment readiness system, ESPORT, currently being pilot tested in Canada. The Essential Skills Portfolio (ESPORT) system is a facilitated and supported Internet-delivered system primarily intended for adults with a high school education or less, intended to assist users to choose an occupation, assess their enabling skills in respect to the chosen occupation, identify and (optionally) remedy skills gaps, and document in a resume their abilities for prospective employers. While this e-learning project was not complete at this writing, this report describes the piloting process, and some already obvious conclusions regarding ESPORT’s technologies, design, delivery, and support models, and training protocols. As anticipated, both the project’s components and the evaluation design have changed, recognizing pressing issues requiring early attention, and responding to lessons learned.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.277
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.011
GPT teacher head0.178
Teacher spread0.167 · 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
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
Published2006
Admission routes1
Has abstractyes

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