The Storytelling External Stakeholder: How Non-Profit Organizations in Supportive Housing Can Help Ensure That Externals Stakeholder Stories End in Connection
Bibliographic record
Abstract
This paper examines the need for organizations to listen to and learn from the stories of their external stakeholders, especially in the context of supportive housing. To this end, this study builds on research conducted by the Dream Team in 2014, which was compiled to create a bill of rights for supportive housing tenants in the Greater Toronto Area. The literature describes many benefits of storytelling for organizations, but often overlooks the stories of external stakeholders in favour of leadership stories. And yet it is widely understood that it is impossible for one story or storyteller to completely capture the essence of any one organization. Ignoring the stories of external stakeholders creates an atmosphere of disconnection and is tantamount to turning a blind eye to unmet market needs. This paper proposes a framework in which a three-pronged linkage between “stakeholder engagement”, “intersectionality” (Crenshaw, 1991), and “organizational attention” (Gómez, 2015) informs an organization’s understanding of external stakeholders’ “exit” and “voice” behaviours (Hirschman, 1970)—and ultimately helps to ensure that the stories of external stakeholders end in connection. The findings of this study reveal that the subjunctive mood may typically be used to tell stories of disconnection, but more research is needed to determine this. Also, the data suggest that the biggest barrier to communication between tenants and supportive housing organizations may be the myth that people with mental illness and/or substance use issues are incompetent children who must be taken care of.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".