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Faculty Use and Non-Use of Electronic Mail: Attitudes, Expectations and Profiles

2006· article· en· W2003482862 on OpenAlexaff
Ananda Mitra, Michael David Hazen, Betty LaFrance, Randall G. Rogan

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsAnticipation (artificial intelligence)PsychologyElectronic mailTest (biology)Medical educationInternet privacyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This paper raises four research questions about the relationships between expectations about the faculty use of e-mail and the level of e-mail usage among faculty. The study uses a survey design to test expectations about technology on several attitude measures. We report that positive expectations about the functionality of technology are related to higher incidence of e-mail use. Furthermore, the results suggest higher existing levels of computer use in general, and that positive anticipation of future use is also related to higher levels of e-mail use in particular. These findings are then used to develop profiles of users and non-users. The results indicate that younger faculty with greater exposure to computers tend to be more frequent users of e-mail than older faculty whose customary communication styles do not include the use of e-mail. Finally it is suggested that expectations about the “promise of technology” are related to the actual use of technology.

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)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.301
Teacher spread0.265 · 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 designObservational
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

Citations29
Published2006
Admission routes1
Has abstractyes

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