MétaCan
Menu
Back to cohort
Record W2019120013 · doi:10.1177/0022487103255499

Toward a Theory of Negativity

2003· article· en· W2019120013 on OpenAlexaff
Heather-Jane Robertson

Bibliographic record

VenueJournal of Teacher Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsCanadian Centre for Policy Alternatives
Fundersnot available
KeywordsIdeologySanctionsNegativity effectPower (physics)FaithInformation and Communications TechnologyPublic relationsSociologyPolitical sciencePedagogySocial psychologyPsychologyPoliticsEpistemologyLaw

Abstract

fetched live from OpenAlex

Teachers are vulnerable to the technopositivist ideology that perpetuates a naive faith in the “promises” of technology. Most teachers have been denied opportunities to explore the motives, power, rewards, and sanctions associated with the unscrupulous marketing of information and communications technology (ICT) and tend to be uninformed about the research that has failed to find a positive relationship between ICT use and student achievement. They remain unaware of the efforts to disguise how devotion to technology necessarily entails retrofitting the purposes and practices of education. This article examines technopositivism as a marketed ideology and follows the marketing strategies that appropriate and redefine educational goals and problems. It explores the alleged link between constructivism and technology and considers how teacher education and standard-setting bodies perpetuate this contestable association. Finally, the article suggests some deliberately critical questions that can be legitimated only if posed by teacher educators.

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)0.008
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.065
Scholarly communication0.0110.015
Open science0.0020.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.051
GPT teacher head0.361
Teacher spread0.310 · 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 designTheoretical or conceptual
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

Citations65
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Teacher EducationSame topicGender and Technology in EducationFrench-language works237,207