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Record W1993969879 · doi:10.4018/jvple.2010040105

Virtual Speed Mentoring in the Workplace - Current Approaches to Personal Informal Learning in the Workplace

2010· article· en· W1993969879 on OpenAlex
Chuck Hamilton, Kristen Langlois, Henry Watson

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Virtual and Personal Learning Environments · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsInformal learningInformal educationSpace (punctuation)IBMTreasureSocial learningValue (mathematics)Workplace learningSociologyPedagogyPsychologyComputer scienceHigher educationEngineering

Abstract

fetched live from OpenAlex

Informal learning is the biggest undiscovered treasure in today’s workplace. Marcia Conner, author and often-cited voice for workplace learning, suggests that “Informal learning accounts for over 75% of the learning taking place in organizations today” (1997). IBM understands the value of the hyper-connected informal workplace and informal learning that comes through mentoring. This case study examines a novel approach to mentoring that is shaped only by virtual space and the participants who inhabit it. The authors found that virtual social environments can bridge distances in a way that is effective, creative and inexpensive. Eighty-five percent of virtual speed mentoring attendees reported that this approach achieved their learning objectives. Participants also reported that virtual social spaces like Second Life® are suitable delivery vehicles for mentoring, and that connecting with people was much easier than via telephone or web conferencing.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
Research integrity0.0000.002
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 head0.069
GPT teacher head0.323
Teacher spread0.254 · 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