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Record W2485361389 · doi:10.1057/9780230602151_2

Culture Clash in Computerized Classrooms

2002· book-chapter· en· W2485361389 on OpenAlexaboutno aff
Ivor Goodson, Michele Knobel, Colín Lankshear, J. Marshall Mangan

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsFocus (optics)Selection (genetic algorithm)Computer scienceEngineering ethicsSoftwareICTSMathematics educationManagement scienceEngineering managementInformation and Communications TechnologyEngineeringPsychologyWorld Wide WebArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

In this chapter, we focus on the introduction of computers into high school classrooms in Ontario, Canada. Many studies of computer introduction focus on issues of technical implementation: issues of how people learn the techniques and, indeed, the "language" of computers. This concentration reflects a certain definition of the problems surrounding the introduction of ICTs into schools. Essentially, the problems are seen as those of overcoming the considerable challenges presented by selection of the hardware and software, technology installation and maintenance, and staff training and development. The methodology of such research, whether qualitative or quantitative, reflects this definition of the problem in its focus. And, as with all research, the definition of the problem and the methodological focus have a great deal of influence on what we "find."KeywordsLesson PlanTeaching StyleSchool SubjectTechnical ImplementationSubject TeacherThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.534
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.281
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2002
Admission routes1
Has abstractyes

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