Bibliographic record
Abstract
Abstract: Industry is dirty, and land, soils, and sites remain toxic even after operations have diminished or closed. Dominant heritage frameworks, aligned with narratives that serve national interests, and environmental plans, have not yet imagined the heritage futures of industrial landscapes— nor the narratives that link the industrial pasts of workers to the present and the future. Significant labor histories are frequently diminished, marginalized, or omitted altogether. Major nation-building industries in Australia, America, Canada and northern Europe were dependent on immigrant labor drawn from Asia, Europe, and South America, and their stories are embedded in the large tracts of industrial sites that have become wastelands of defunct and demolished structures. “Dirty” extends onto a linguistic terrain of “dirty histories” and the silencing of particular histories parallel the masking of environmental toxicity. Focusing on the Port Kembla steelworks in Australia, this article examines immigrant industrial labor history and develops a perspective from which to rethink heritage practice and the theoretical development of critical carbon. If critical carbon is conceptualized as a matter that concerns both the exploitation of land and of peoples, this article argues that heritage practice needs to develop projects around immigrant heritage sites such as the steelworks.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".