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Record W4327598172 · doi:10.4324/9781003364627

Will Schooling Ever Change?

2023· book· en· W4327598172 on OpenAlexaff
Piotr Mikiewicz, Marta Jurczak-Morris

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsVictoria Park
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

This book is an insightful meta-narrative about schooling which explores the global natural experiment of the COVID-19 pandemic and its potential impact on school culture. The proposed book discusses how the abrupt and somewhat forced digital transformation of schooling on a global scale (caused by the COVID-19 pandemic) did not change the educational status quo. It states that online teaching and learning failed to transform the role of the key school actors, students and teachers as well as the relationship between them, despite megatrends such as digitalisation, automation and the development of artificial intelligence. This focus text discusses why the global experience of distance education did not translate into a significant qualitative change and provides a theoretical framework which enables the reader to interpret and explain the processes that occurred during distance education, as well as understand why extraordinarily little (if nothing) has changed in school culture. It will appeal to scholars and students from the sociology of education and from education studies, particularly those interested in school culture, innovation in education, online teaching and learning, curriculum studies and education policy.

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)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.011
Scholarly communication0.0070.011
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0180.004

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 head0.135
GPT teacher head0.405
Teacher spread0.270 · 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
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

Citations2
Published2023
Admission routes1
Has abstractyes

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