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Record W6988095335

We've got to do better

2014· article· en· W6988095335 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Online (University of Wollongong) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCyberloafing and Workplace Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNothingQuarter (Canadian coin)The InternetALARMClothingProductivityRidiculous
DOInot available

Abstract

fetched live from OpenAlex

Katherine Albrecht and Katina Michael explains that the time spent in electronic vectors of nothingness is contributing to the decay of our meaningful, relationships. Barring sleep, people have just 16 hours each day to live their conscious lives. If they spend 11 of them online, at a console, or in a game, that's 69% of their waking lives. Most of us legitimately require screen time for work, but we often get stuck there. All those online hours come at a high cost. We feel pressured, like there is never enough time to get everything done. The productivity void of all these wasted hours is already beginning to alarm U.S. employers, as analysts bemoan that employees spend one quarter of their online time at the office on non-work related Internet surfing, thus squandering an average of five hours per week.

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)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0080.010
Open science0.0010.006
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0450.022

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 head0.064
GPT teacher head0.380
Teacher spread0.316 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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