Bibliographic record
Abstract
As an adoptee, I am haunted by what Lifton (2009) calls the Ghost Kingdom, a place filled with the spectres of the ancestors I have been disconnected from. Derrida (1993), with his notion of hauntology, tells us that we must learn to speak to ghosts, and that by doing so we will learn to live. I am on a journey to speak to the ancestors of my birth father who were Ngāi Tahu (Māori), and through this, to make meaning in my present and future (Carsten, 2000). I am using embroidery as a medium to speak to, and with, my great-great-grandmother, Elizabeth (Fitzpatrick & Bell, 2016), working in a craft vernacular that would have been deeply familiar to her. This paper will discuss how the methodology of autoethnography, informed by adoption scholarship and feminist studies of craft, has led me to stitch work that engages with craft tradition, and speaks to loss, identity and belonging.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".