Bibliographic record
Abstract
My mother grew up in a family of 11 siblings, all born in 11 years and 11 days, in a small town in Nujio’qonik, Ktaqmkuk (Bay St. George, Newfoundland). Our family is French, Mi’kmaw, and Irish/English, and are some of the best storytellers I know. Through a series of semi-structure interviews with ten of the siblings, this research project set out to study family stories, passed down through generations, and the importance these stories play in fostering connections. The project continued an ever-growing process of building-up our own stories and understandings of our connection to home, to Nujio’qonik, to who we are and where we come from, and is set against the backdrop of complicated personal and community journeys of identity and recognition of Ktaqmkukewey (Newfoundland) Mi’kmaq people. At the core of the research, I was looking to study connection and stories, and, just like a story should be, the process was one of twists and turns, weaving and unravelling, re-building and re-telling. As this abstract gives a glimpse of, this thesis is not so much a clean summary of the results and findings, but rather a story in itself – a story of the process of finding connections and yet not studying them, of taking the data from the academy and re-creating a collection of stories that no longer exist in this space. And, like so many good stories I have heard, there’s a trickster, in this case Blue Jay, who hops in regularly to remind me of what I am missing, to keep me laughing [often at myself], and to guide me through not only the research process, but the very words you are reading here now.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.018 |
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".