Born archival: The ebb and flow of digital documents from the field
Bibliographic record
Abstract
Facilitated by an infusion of funding from philanthropic sources, descriptive linguists have been galvanized to document the world's languages before they disappear without record. Linguists have responded to the "crisis of documentation" (Dobrin, L. M. & Berson, J. (2011), "Speakers and Language Documentation", in The Cambridge Handbook of Endangered Languages, P. K. Austin & J. Sallabank (eds), Cambridge University Press, Cambridge, pp. 187–211) by entering into increasingly collaborative partnerships with speech communities, producing "documents" that have both local relevance and academic integrity. The growth in access to digital recording technology has meant that contemporary research initiatives on endangered languages are not only born digital, but often birthed straight into an archive. Yet heritage collections of recordings made by ethnographers and linguists in the past are ever more endangered, becoming orphaned when their collectors die or fragmented into their component parts based on the medium of documentation when they are finally archived. Drawing on fieldwork in Nepal with a community speaking an endangered Tibeto–Burman language, and reflecting on the decade I have spent directing a digital humanities research initiative—the Digital Himalaya Project—I discuss how linguists and anthropologists are collecting, protecting and connecting their data, and how technology influences their relationship to documents.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".