Registered Partnerships: A Model for Partnership Recognition (Disponible en Français: Les unions libres enregistrées: Un modèle de reconnaissance des rapports personnels)
Bibliographic record
Abstract
The principal objective of this paper is to provide a factual and an analytical examination of registered partnerships. It was submitted to the Law Commission of Canada as part of an exploration of close, personal relationships formed by adults. As such, this research could provide a better understanding of the actual and potential uses of registered partnerships, as a means for creating better conditions for adults in close personal relationships to declare their commitments, obligations and responsibilities to each other.The paper is divided in three main sections. Following the introduction, Part II categorizes and describes the various types of registered partnership models that have been established in different jurisdictions around the world. The Annex, which provides factual information about existing registered partnership models in a table format, provides additional and complementary information and should be consulted in conjunction with Part II. Part III reviews current uses of registered partnerships to assess the value of opting for such a model. Academic and activist debates are examined to outline the benefits and pitfalls with registered partnerships as a model of legal recognition for either conjugal or non-conjugal relationships. Part VI looks to situate the issue in the Canadian context by contrasting the debates surrounding registered partnerships in other jurisdictions. This last section will assess whether registered partnerships add any value if same-sex couples are no longer barred from marriage.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".