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Record W7025368455

Why Kids Find Computing Jobs To Be A Turnoff

2023· other· en· W7025368455 on OpenAlexaboutno aff

Bibliographic record

VenueArrow - TU Dublin (Technological University Dublin) · 2023
Typeother
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceFalling (accident)Information technologyJob lossOutsourcingQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Ireland is a solid player in the global computing sector and has done a great job attracting high-tech names to Ireland, with eight of the ten top global information technology companies establishing a important presence here. Computing as a career can be financially and personally rewarding and, coupled with prospects across diverse industries, who wouldn't consider it?.\nBut there is concern among employers in the sector about the inability to fill computing jobs. Even with tech companies realigning their overall workforce and ongoing redundancies, there are so many vacancies at the moment. Falling numbers taking computing courses at third level and low female uptake really don’t help the problem.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.126
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0150.004
Scholarly communication0.0100.010
Open science0.0020.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.1260.048

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 head0.048
GPT teacher head0.217
Teacher spread0.169 · 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
Published2023
Admission routes1
Has abstractyes

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