Bibliographic record
Abstract
If you type “what does [insert any religion here] say” into Google, the search bar will auto-fill to the top four or five options. One of the top options will almost always be “what does [insert random religion here] say about homosexuality, gay marriage, or marriage”. Questions regarding same-sex marriage and LGBTQ identities have also become particularly popular for public research and polling organizations. This comes at a time when same-sex marriage, while federally legal, is still being debated and new “religious” freedom bills are being passed in response. Pew Research did a study and found that as of July 2015, Buddhists in the U.S. had no conclusive stance on same-sex marriage. Questions about religion and LGBTQ identities continue to be a contentious intersection. However, the discussion surrounding “what do religions say” are leaving out a crucial piece of the puzzle; they are failing to ask “how” do LGBTQ religious practitioners think of their own identities. Many LGBTQ people across the U.S. have found a home in various sects of Buddhism. This will be analyzed through various personal essays by Buddhist practitioners from Queer Dharma Volume 2. Ultimately, LGBTQ practitioners are drawn to Buddhism because of its non-duality, its cultural malleability, and its teachings that relate to impermanence.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".