Bibliographic record
Abstract
: The conclusion briefly revisits the main findings of the book and reasserts the benefits of a combinatory framework for the study of media artefacts. It also tentatively outlines a new approach to the study of phonography, moving from the spectral (or hauntological) conceptual framework (with its insistence on the trace and sonic inscriptibility) to an embodied tactile framework. Keywords: phonography, hauntology, senses, memory, labour, embodiment In his poem ‘New Year's Letter (1995)’, Canadian poet Craig Poile writes about the time-dilating experience of painting a friend's home, attentively coating the worn and damaged walls with fresh layers of paint to give them a new lease of life. The poem establishes a parallel between the exteriorised process of painting and the alterations affecting the narrator's mind, as he reflects on time past and the shedding of former selves. Both outer and inner changes are carefully recorded in the medium of the poem, ‘stroke by stroke’. ‘New Year's Letter’ evokes the erasure of past selves and moments, the taking leave of old friends and the emergence of novel relations. The final step, the poet notes, is to varnish the walls with a thin layer of shellac. ‘“Shellac!”‘, he exclaims in the last stanza, ‘seals in the icons as I leave, turning from / the traces of hands going dull on the wall, / the tactile moment where a life takes shape’. The scene is momentarily sealed, yet not completely arrested, and the last word is one of quiet wonder and suspension – an intimation of the unknown, unspent and yet unlived shape of things to come. Poile leaves the future open: the final coat of shellac is still drying as the poet departs. Poile's poem about painting reminds me of what material culture theorist Thomas J. Schlereth once wrote about the practice and demands of historical writing, comparing it to the carpenter's craft. There is something patient, repetitive, at once technical and intuitive, abstract and embodied, about building a text – about seeking to assemble something which, though inevitably imperfect, will work and stand by itself (at least for a little while).
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".