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Record W2198214919

23 Things Revisited: Participant perceptions of a staff development program over a year later

2015· article· en· W2198214919 on OpenAlexaffabout
Christine Neilson, Jamie Sofoifa, Rachel Sarjeant-Jenkins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFlexibility (engineering)PerceptionPsychologyMedical educationPublic relationsManagementPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

23 Things programs have been used around the world to help educate library workers about web 2.0 applications. This paper describes the results of two feedback surveys for a 23 Things program that was offered to library employees at the University of Saskatchewan: the first survey, conducted at the time of program completion, and a follow up survey, conducted a year and a half later to assess whether participants were applying what they learned in the program, and if so, how.  Generally speaking, respondents indicated that the program was beneficial, and all but 3 of the respondents from the second survey had applied some of what they learned through the program, either personally or professionally.  Despite being an independent learning activity, social aspects that arose organically as participants worked their way through the program seemed to be valued by program participants as much as the newly acquired knowledge from the lessons. The semi-facilitated format, in addition to the flexibility of completing the lessons at a convenient time and place, was key to the program’s success. Organizations that wish to transition long-time employees from the print-based working world to the digital world should consider making 23 Things style learning opportunities a regular part of their staff development activities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.305

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 communication0.0000.001
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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes2
Has abstractyes

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