Bibliographic record
Abstract
Research Article| April 01 2022 Kinship in the (Un)Making: When Lover Does Not Sound Right Özgül Akıncı Özgül Akıncı Author’s email: ozgulakinci@gmail.com Search for other works by this author on: This Site PubMed Google Scholar Journal of Autoethnography (2022) 3 (2): 254–257. https://doi.org/10.1525/joae.2022.3.2.254 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Özgül Akıncı; Kinship in the (Un)Making: When Lover Does Not Sound Right. Journal of Autoethnography 1 April 2022; 3 (2): 254–257. doi: https://doi.org/10.1525/joae.2022.3.2.254 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of Autoethnography Search 1. We are at home. O2 and I. We are participating in an online gathering. I share a sweet anecdote of O and I with the group. Someone asks, “Are you, two, mother and daughter?” with affection in her voice. “No,” I say, “we are lovers.” Affection is replaced by loud laughter immediately punctuating the end of the conversation while my answer “we are lovers” lingers in our virtual space. Our relationship status with O is suddenly out in the open, giving hiccups to always-already functioning heteronormative mind. A sense of pride accompanies being gently forced to make a statement about my intimate bond. I know that we are at a safe space. Yet, I also know that erotic love between women always has a surplus value in the language of desire, waiting to be claimed by, be rescued from, and be uttered performatively. This is the queer “bound... You do not currently have access to this content.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".