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Record W27505678

Testing a Diffusion of Innovations in Education Model (DIEM)

2005· article· en· W27505678 on OpenAlex
Mark K. Warford

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue˜The œinnovation journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsForeign languageCLARITYInnovation diffusionLanguage educationMathematics educationSociologyPsychologyPedagogyPolitical scienceBusinessMarketing
DOInot available

Abstract

fetched live from OpenAlex

Following is a report on a questionnaire study based on the Diffusion of Innovations in Education Model (DIEM), which synthesizes research on educational innovations. The social system under study included foreign language teacher educators in eleven Southeastern states (N=83). Regional foreign language teacher educators were targeted for gathering data regarding the ACTFL (American Council on the Teaching of Foreign Languages) Proficiency Guidelines (1986), a language teaching innovation. In analyzing results, inferential statistics tested the weight of some of the DIEM’s predictions about the nature of educational change. In terms of the model’s predictions, state mandates appear to hinder rather than facilitate adoption. However, results support the DIEM claim that innovation knowledge is associated with its adoption. While the DIEM provides conceptual clarity to research on change in educational settings, its usefulness as a way to explain and predict the success or failure of educational innovations in attaining adoption remains to be verified.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.386
Teacher spread0.305 · 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