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Record W2129917600 · doi:10.36510/learnland.v3i2.348

Where Are We? The State of Education

2010· article· en· W2129917600 on OpenAlexaffvenue
Sharon Rich

Bibliographic record

VenueLEARNing Landscapes · 2010
Typearticle
Languageen
FieldComputer Science
TopicEducational Challenges and Innovations
Canadian institutionsNipissing University
Fundersnot available
KeywordsContext (archaeology)Adaptation (eye)State (computer science)Public relationsWork (physics)Key (lock)SociologyPolitical sciencePedagogyPsychologyComputer scienceEngineeringGeography

Abstract

fetched live from OpenAlex

Today the questions that should be asked about schools and schooling are those that take into account the social context in which we live. We need to attend to the world outside of the closed context of the "system" and recognize the ways in which the world is interrelated. We need to understand that each and every student comes to the classroom with a biography and a way of being in the world. For today’s young learners that world is a wired one in which social interaction can be conducted anywhere, any place, or anytime. A key challenge for educators is to adapt the institutions in which they work to meet the emerging reality of the connected environment. If we do not manage to make this adaptation, then the future of public education is bleak.

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)0.005
metaresearch head score (Gemma)0.008
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.025
Scholarly communication0.0250.033
Open science0.0010.005
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0170.003

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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".

Quick stats

Citations2
Published2010
Admission routes2
Has abstractyes

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