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Record W4243452714 · doi:10.1007/978-94-6091-915-2_1

Section One

2012· book-chapter· en· W4243452714 on OpenAlexaff
D. W. Livingstone, David Guile

Bibliographic record

VenueSensePublishers eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSection (typography)Knowledge economyLifelong learningWork (physics)Public relationsKnowledge managementPolitical scienceBusinessEngineeringComputer scienceMechanical engineeringLawAdvertising

Abstract

fetched live from OpenAlex

As suggested in the General Introduction, the notion of the emergence of a knowledge economy has become so widespread that it is now commonly assumed in both policy discussions and the mass media. Many advocates have heralded the knowledge economy as transforming the nature of both work and learning. A shift from materials handling to processing of information aided by global electronic technology is seen to be converting much of the labour force into knowledge workers; a direct consequence is that workers must devote more of their efforts to pursuit of lifelong learning to meet the growing knowledge demands of their jobs. The problem, as the chapters in this section make clear, is that this notion remains far from reality.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.449
Threshold uncertainty score0.641

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.5510.346

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 head0.052
GPT teacher head0.277
Teacher spread0.225 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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