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Record W2901607619 · doi:10.3138/cpp.2018-001

Information and Communication Technology Talent: The Skills We Need—Framing the Issues

2018· article· en· W2901607619 on OpenAlexaffvenueabout
Ross Finnie, Richard Mueller, Arthur Sweetman

Bibliographic record

VenueCanadian Public Policy · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsMcMaster UniversityUniversity of LethbridgeUniversity of Ottawa
Fundersnot available
KeywordsInformation and Communications TechnologyEconomic shortageFraming (construction)Context (archaeology)Conceptual frameworkBusinessMarketingPublic relationsSociologyPolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

This article provides a conceptual framework for discussing information and communication technology (ICT) skills shortages in a context in which having a sufficient supply of skilled ICT workers—both inside the ICT sector and more broadly—is critical to the performance of the Canadian economy. We start with an outline of a simple model of how ICT skills shortages might be manifested in labour market signals, such as rising wages, and then how, in response to these signals, workers already in the labour market should adjust, whereas young people making schooling and career decisions would be expected to skew toward ICT areas. We then discuss some of the reasons these dynamics might not follow this model, and therefore how ICT skill shortages could potentially endure over time. The article sets the stage for the other articles in this special issue, which address these and other issues related to ICT skills from a mix of traditional academic and industry perspectives.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 designTheoretical or conceptual
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

Citations9
Published2018
Admission routes3
Has abstractyes

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