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Record W4390079918 · doi:10.32316/hse-rhe.2023.5141

Making the Computer Fit for School

2023· article· en· W4390079918 on OpenAlexvenueno aff
Rosalía Guerrero Cantarell, Carmen Flury

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

As a result of the computerization of various workplaces and the increased presence of micro- computers in society, several countries around the globe took steps in the 1980s to introduce computers into schools. In certain countries, such as East Germany (GDR) and Sweden, this meant developing a purpose-built computer centrally to raise pupils’ level of competence in informatics. As part of this process, the microcomputer became the epitome of educational technology. In this article, we investigate the process by which the microcomputer became an educational technology in the minds of the politicians and pedagogues involved in the projects. We argue that the national projects that the GDR and Sweden embarked upon express the dominant views of the respective state authorities in relation to the ideal relationship between computer technology, society, and education. Through a historical comparison by contrast of contexts, this article shows the sociotechnical imaginaries that prompted the two countries to initiate a strategy to bring computer technology into schools.

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)0.004
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 score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0070.007
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0300.012

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 head0.243
GPT teacher head0.448
Teacher spread0.205 · 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
Published2023
Admission routes1
Has abstractyes

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