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Record W3116064833 · doi:10.32674/jis.v10i4.3169

(Re)Learning to Live Together in 2020

2020· article· en· W3116064833 on OpenAlexaboutno aff
Darla K. Deardorff

Bibliographic record

VenueJournal of International Students · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Media studiesIsolation (microbiology)Front (military)PandemicCoronavirus disease 2019 (COVID-19)Social distanceDistancingHistorySociologyPolitical scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

Ten years ago, the world was quite a different place with the devastation of a 7.0 magnitude earthquake in Haiti, the exuberance of the 2010 Winter Olympics in Vancouver, the release of the first iPad, the launch of Instagram, and the beginning of Arab Spring. Fast forward to 2020 and not only is it the 10th anniversary of the Journal of International Students, but the world is facing unprecedented times with a global pandemic that has illustrated the interconnectedness of humankind like never before. We have all been reminded of the power of human connection as we experience isolation, confinement, social distancing, and even fear. We have witnessed powerful images of front-line workers giving their all, and neighbors in cities and towns across the world connecting from balconies and through windowpanes. These images have reminded us how much our lives depend on those around us, and how important it is that we renew our efforts in learning how to live together.

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)0.004
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0120.014
Open science0.0020.024
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0610.045

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 head0.038
GPT teacher head0.375
Teacher spread0.336 · 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
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

Citations14
Published2020
Admission routes1
Has abstractyes

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