A Human Rights and Legal Analysis of the Understanding Our Roots Report
Bibliographic record
Abstract
In October 2023, the University released Understanding Our Roots - Nstikuk tan wtapeksikw Report written by the Task Force on Settler Misappropriation of Indigenous Identity. The Report recommends the creation of a Standing Committee who would verify claims to Indigenous identity by students, faculty and staff seeking to benefit from any opportunity at the University that prioritizes access for Indigenous peoples, as well as investigate and recommend sanction in cases of suspected academic fraud whereby an individual assumes an Indigenous identity. The Report does not address or respond to potential legal issues and rights violations arising from its recommendation. To our knowledge, no legal analysis has been conducted on the implications of the proposed verification process. In the hopes of encouraging more nuance and circumspection on this issue we offer the following analysis, informed by our respective areas of expertise.\nOur concerns about the Report can be distilled into the following: The Task Force was comprised of members unqualified in the historical and legal complexity of Indigenous identity; The Task Force’s engagement process may have violated research ethics, was grossly inadequate in both scope and timeline, and may be in breach of procedural fairness standards; The proposed verification process conflates self-identification and uncertainty over Indigenous identity with academic fraud; The proposed verification process is underinclusive and discriminatory by overlooking several categories of Indigenous people who have legitimate, legally-supported claims to being Indigenous, including the large and growing Non-Status First Nation population in the region, and members of Indigenous collectives like NunatuKavut Community Council and the Peskotomuhkati Nation at Skutik; The proposed verification process fails to support Indigenous self-determination over identity because it centers Indigenous identity on official federal government recognition, which is not in keeping with constitutional law, domestic and international human rights, including the United Nations Declaration on the Rights of Indigenous Peoples; The proposed verification process exposes Dalhousie to liability, including in relation to labour, employment, and human rights law; Implementation of the verification criteria is already occurring without appropriate review and legal analysis; The verification process will actively cause harm to Indigenous students, faculty, and staff and compromise current and planned work; and The University already has all the necessary tools to address situations of academic fraud of Indigenous identity, as well as to respond to the distinct concern of privileging individuals whose self-identification as Indigenous rests solely on having distant ancestry over those with legitimate, legally-supported claims to being Indigenous. Thus, no new process is necessary.
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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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.029 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".