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Record W3124110504 · doi:10.59876/a-p1c9-8dvg

De la formation e-Learning à une démarche heuristique de l'apprentissage organisationnel

2007· article· en· W3124110504 on OpenAlex

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

VenueManagement international · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Technology and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)SociologyAdaptation (eye)Field (mathematics)Social learningKnowledge managementHeuristicPsychologyEpistemologyComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

E-learning experiments carried out in the field of training often prove disappointing. Therefore, adoption motives, the assessment of which is a growing concern for companies, have to be examined. Which learning methods encourage e-learning adoption? Can new technologies encourage organisational learning? From an epistemological point of view, these questions are in keeping with the social perspective which is developing in the field of information systems. The Grounded Theory is applied to interviews carried out with companies following an inductive approach. From a heuristic point of view, the framework of analysis identifies several learning methods which may be pragmatic, cognitive or operational. It appears that the e-learning experiments depend as much on their adaptation to the environment according to a strategic approach as on the social interactions with technology in managerial practices. [PUBLICATION ABSTRACT]

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)0.003
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: none
Teacher disagreement score0.933
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.307
Teacher spread0.296 · 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