MétaCan
Menu
Back to cohort
Record W3032823759 · doi:10.1057/s41267-020-00335-9

International HRM insights for navigating the COVID-19 pandemic: Implications for future research and practice

2020· editorial· en· W3032823759 on OpenAlexaff
Paula Caligiuri, Helen De Cieri, Dana Minbaeva, Alain Verbeke, Angelika Zimmermann

Bibliographic record

VenueJournal of International Business Studies · 2020
Typeeditorial
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicInternational business2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Organizational culturePolitical scienceEconomicsManagementPublic relationsVirologyMedicineOutbreak

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.023
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.033
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.086
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0060.004
Science and technology studies0.0080.007
Scholarly communication0.0220.010
Open science0.0040.005
Research integrity0.0330.037
Insufficient payload (model declined to judge)0.0170.007

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 head0.252
GPT teacher head0.539
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations703
Published2020
Admission routes1
Has abstractno

Explore more

Same venueJournal of International Business StudiesSame topicInternational Student and Expatriate ChallengesFrench-language works237,207