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Record W795780379 · doi:10.22004/ag.econ.314684

Trends in Logistics Professional Development: Skills vs. Concept - Case Study: The Logisitics Institute

2021· article· en· W795780379 on OpenAlexaboutno aff
Victor S. Deyglio, David M. Cape

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsCorporationBusinessCompetence (human resources)Supply chainHuman resourcesGovernment (linguistics)Humanitarian LogisticsManagementMarketingFinanceProcess managementEconomics

Abstract

fetched live from OpenAlex

The real challenge today is not supply chain management, but Logistics strategic leadership. To meet this challenge, the Logistics community in Canada founded the Canadian Professional Logistics Institute as a not-for-profit corporation under Part 2 of the Canada Corporations Act, originally November 01, 1989 and with Supplementary Letters Patent February 28, 1992. Strategic leadership builds on competence, motivation, and the commitment to life-long learning. In meeting this challenge, the Logistics Institute co-ordinates activities and efforts among industry, government and education stakeholders in order to establish a Logistics profession, develop comprehensive training in Logistics, define Logistics career opportunities for a global economy and sustain Logistics human resource development in Canada. For the covering abstract of this conference see IRRD number 872753.

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)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.063
GPT teacher head0.289
Teacher spread0.226 · 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
Published2021
Admission routes1
Has abstractyes

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