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Record W7133059774

How we learned at work: an ethnographic study of e-learning in a retail setting

2004· dissertation· W7133059774 on OpenAlexaboutno aff
Wendy Mae Hardman

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyContext (archaeology)Coping (psychology)Product (mathematics)Front lineMatching (statistics)Focus group
DOInot available

Abstract

fetched live from OpenAlex

As the use of computer-based training for workplace safety, regulatory standards and product knowledge grows, this potentially affects all employees, including some who may have limited experience and skills in using this medium. To that end, this ethnography studied a group of front line workers in a Canadian retail setting who completed a series of courses in a self-study Web-based training program, with are eye to understanding the workers' perspective. Surfacing from the analysis of the data, gathered during interviews and observations of a group of twelve workers in a retail organization, were four major findings: (1) an increased sense of self-worth that participants experienced from completing e-learning courses and achieving awards, (2) an improved ability to help others because of the knowledge the participants gained from the e-learning courses, (3) the use of the courses in the e-learning program as a source of information for reference purposes, and (4) the value of providing participants with personal choices including what, where and when they used the e-learning program. Two minor findings emerged: the rules of the e-learning program and the coping strategies developed by participants. The e-learning context of this study consisted of four components: retail staff, management, customers and the e-learning program itself. Factors emerged from each component to influence the retail staffs' progress in the program, including an in-house champion, a structure for learning, personal control, personal development opportunities, and their customers' demand for information. Other factors contributing to the participants' success in the program included: matching the structure to the learners' situation, developing an engaging presentation, providing regular feedback and reinforcement and offering reliable course content. All factors needed to be adequately addressed for the participants to progress effectively in this e-learning environment.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.011
Scholarly communication0.0060.007
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.178
GPT teacher head0.517
Teacher spread0.339 · 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 designQualitative
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
Published2004
Admission routes1
Has abstractyes

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