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Record W2496381925 · doi:10.1007/978-94-6300-438-1_10

Decision Making and Problems of Evidence for Emerging Educational Technologies

2016· book-chapter· en· W2496381925 on OpenAlexaff
Erika E. Smith, Richard Hayman

Bibliographic record

VenueSensePublishers eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEmerging technologiesEngineering ethicsPolitical scienceKnowledge managementEngineeringManagement scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

To support what is commonly referred to as twenty-first century learning, those in decision-making roles are often urged to quickly adopt and integrate the newest educational technologies and abandon older processes—or risk becoming obsolete. This impetus to quickly adapt can be witnessed in the discourse surrounding the impact of technologies in today’s educational landscapes.

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)0.470
metaresearch head score (Gemma)0.700
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.470
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4700.700
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.004
Bibliometrics0.0210.017
Science and technology studies0.0060.048
Scholarly communication0.0300.049
Open science0.0130.013
Research integrity0.0220.021
Insufficient payload (model declined to judge)0.0090.001

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 head0.098
GPT teacher head0.375
Teacher spread0.277 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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Same venueSensePublishers eBooksSame topicOnline and Blended LearningFrench-language works237,207