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Record W4294351386 · doi:10.36834/cmej.73818

Five ways to get a grip on organizational logistics, backends, and workflows

2022· article· en· W4294351386 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Education Journal · 2022
Typearticle
Languageen
FieldComputer Science
TopicSpreadsheets and End-User Computing
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsWorkflowHumanitiesPolitical scienceKnowledge managementComputer scienceBusinessBusiness administrationProcess managementSociologyManagementPhilosophyEconomicsDatabase

Abstract

fetched live from OpenAlex

Organizational backends and logistics are often complex and many institutions set-up their workflows based on manual and tedious processes that negatively shape their interactions with stakeholders. Incorporating new technologies can be intimidating. However, a plethora of financially and technically accessible resources that do not require any coding knowledge, can be utilized by institutions to enhance their organizational workflow and stakeholder experience. Guided by our own learning experiences in optimal logistical set-up and user design, we wish to highlight five effective and easily implementable tricks to aid higher institutions and student groups in healthcare to accomplish their administrative duties.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.763
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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