Scales of justice: putting remembrance back on the map in Palestine and Mi’kma’ki
Bibliographic record
Abstract
This paper considers two remarkable efforts to counter the repressive effects of ‘colonial cartography’: the Nakba map project in Israel, and the Ta’n Weji-sqalia’tiek Mi’kmaw Place Names Digital Atlas and Website Project in Mi’kma’ki (Nova Scotia, Canada). Undertaken by the Israeli NGO Zochrot (‘remembering’ in Hebrew), the Nakba ‘counter-mapping’ project seeks to challenge settler foundation myths utilized to perpetuate ongoing oppression. The Ta’n Weji-sqalia’tiek Mi’kmaw Place Names project deploys cartographic revelation – the recovery of erased place names – as a mode of cultural reclamation, confronting settlers with the natural and human realities of the ‘world’ they ‘discovered.’ In addition, by delineating the legends and lore encoded and embedded in those place names, the project helps place the Mi’kmaw language and worldview back ‘on the map’ of remembrance and dialogue. Linkages between the two examples of settler occupation are highlighted through a consideration of the ways the Canadian government assists with the ongoing oppression of Palestine, as well as the ways Palestinian-Indigenous solidarity efforts seek to resist entrenched settler-centric narratives (mental maps). The case studies are presented as part of a necessarily larger, on-going effort to give voice to those long silenced by the hegemonic ‘truth’ of settler societies.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".