Single-parent Families in Canada: A Positive Discourse Analysis of Non-profit Organizations' Websites
Bibliographic record
Abstract
Family patterns have diversified considerably in the last sixty years going beyond the married nuclear family (a married couple with children) and single-parent families are a now widely recognised phenomenon and family assemblage (UN 2017). Yet, single parents face not only several financial and practical challenges, but also social stigma and stereotyping (Sussman and Hanson 1995; Zartler 2014). In the context of Canada, despite the fact that the proportion of families with children has remained rather stable over the decades, the types of families with children have changed consistently, and over one-fifth of Canadian children are being raised by a lone parent. Against this backdrop, charities and associations are supporting single parents through a series of actions to reduce social stigma and make services more accessible to them. This study specifically aims to investigate how new concepts of family are discursively construed and conveyed, to frame single-motherhood from a different and more positive perspective. Following the tradition of Social Semiotics (Kress and van Leeuwen 2021) and research on Positive Discourse Analysis (Martin 2004; Bartlett 2012), this work analyses a range of multimodal resources available on the websites of three Canadian non-profit organizations. In particular, it focuses on how single-parent families are represented assuming that the resulting discursive construal can work to eradicate persistent cultural and social stereotypes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".