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Record W6931065636 · doi:10.5281/zenodo.3827550

Equity, Diversity and Inclusion in the Canadian Astronomical Society in the Next Decade

2019· article· en· W6931065636 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsOkanagan College
Fundersnot available
KeywordsGovernment (linguistics)White paperCommitInclusion (mineral)Diversity (politics)HarassmentProfessional associationWork (physics)General partnership

Abstract

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The equity, diversity and inclusion (EDI) landscape in professional astronomy has evolved tremendously over the last decade, both in Canada and around the world. On one hand, revelations of systematic harassment of junior astronomers by their senior advisors or colleagues as well as studies demonstrating significant demographic biases in the peer review process have both made international headlines, illustrating just how much work remains to be done to achieve EDI in academia. On the other hand, increased awareness of EDI issues within professional organisations and governments worldwide has resulted in innovative new policies and program launches that may well lead to meaningful progress. Change is indeed underway in Canada as universities revise their ethics statements and codes of conduct, EDI-focused committees emerge to advise organisations such as the Canadian Association of Physicists (CAP) and the Canadian Astronomical Society (CASCA), and the federal government attaches requirements regarding EDI reporting and achievements to funding. This white paper addresses EDI considerations for CASCA in the next decade from the perspective of CASCA's Equity and Inclusivity Committee (EIC).We emphasize that the recommendations below are by no means exhaustive, but instead should be considered in conjunction with other EDI-related recommendations submitted as part of LRP 2020. The goal is that in aggregate, this and other white papers span the spectrum of EDI issues that are most relevant to Canadian astronomy in the next decade. We make the following recommendations: The CASCA Board should commit to creating and maintaining a comprehensive national database of its members.This database is of urgent importance since it would allow searches for members by other members and by the public, and would also meet the needs of the community in collecting anonymous self-reported demographics data. The CASCA Board should direct funding toward hiring professionals to create a modern database that serves this dual purpose, and that its members can trust and use easily. The CASCA Board should prioritize updating the CASCA Mission and Ethics statements and creating a Values Statement and a Code of Ethics. These documents would provide the basic framework for CASCA-developed EDI initiatives, and should therefore be self-consistent and enforceable; this may require funding to accomplish. The CASCA Board should prioritize EDI training within the CASCA membership. We recommend that committee members complete EDI training as part of their service; Canadian-made training programs that touch on many relevant EDI issues are freely available and their uptake would be a no-cost first step. The CASCA Board should also consider funding EDI-focused plenary sessions that bring in trained EDI experts at CASCA Annual General Meetings. The CASCA Board should endorse the exploration of a national mentoring strategy for early-career astronomers. Mentorship programs have been successfully implemented in other communities, and would build relationships across the country and enhance support for students, postdocs and junior faculty outside their home institutions by pairing them with a mentor closely aligned with their personal or professional experiences. The CASCA Board should commit to the principles of the Dimensions Charter, and encourage its members and their institutions to do the same. CASCA should encourage members at the chosen Dimensions pilot project institutions to participate in that study over the next two years and then assess the outcome with respect to its own EDI values and goals.

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)0.037
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
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 score0.994
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.087
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0340.016
Scholarly communication0.0310.010
Open science0.0060.015
Research integrity0.0130.010
Insufficient payload (model declined to judge)0.0130.002

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 head0.094
GPT teacher head0.322
Teacher spread0.228 · 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.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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