The opportunities and limits of open science for LGBTIQ+ research
Bibliographic record
Abstract
Abstract The open science (OS) movement has the potential to fundamentally shape how researchers conduct research and distribute findings. However, the implications for research on lesbian, gay, bisexual, trans, intersex, and queer/questioning (LGBTIQ+) experiences present unique considerations. In this paper, included in the special issue on Reimagining LGBTIQ+ Research, we explore how the OS movement broadens access to and comprehension of LGBTIQ+ experiences while simultaneously imposing limitations on the representation of these identities and raising concerns about risks to LGBTIQ+ researchers and participants. Our research focuses on three facets of the OS movement. First, we examine practices related to open data, which advocates that data should be accessible to other researchers to analyze. Yet, providing access to such data challenges may compromise trust between the research team and study participants. Second, we examine practices related to open replicable research, particularly as it has the potential to both highlight and erase the experiences of groups within the LGBTIQ+ community. Finally, we consider how open access, making scholarly articles free to the public, may help educate a broader audience on the lived experiences of LGBTIQ+ people, but in regions where these identities remain heavily stigmatized and/or criminalized, access may be blocked or individuals could be penalized for retrieving this information.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.368 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.137 |
| Scholarly communication | 0.042 | 0.045 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".