MEDIAting Immigration Stories at Canada's Pier 21
Bibliographic record
Abstract
The collection of digital stories (DS) available at Pier 21 is the outcome of digital storytelling workshops held across Canada that were offered by the museum as part of a project that involved facilitators from Community Story Strategies. Newcomers from ten cities participated in three-day workshops and contributed their immigration stories to the project, creating a two to five minute digital story that shares their immigration experience in a personal way. What is of interest here, is that DS may broaden the scope of oral history records as a tool to tell alternative stories, using digital media to record, collect, and disseminate very personal contributions to the broad immigration experience. My objective in the present paper is to explore a sample selection of these stories, specifically, the manner in which the subjective positioning of the narrators reverberates through language. Different issues are addressed, among them, the linguistic resources through which positive and negative evaluations are inscribed in the texts and the ways in which personal orientations are negotiated with the putative reader/listener. The discussion is supported by examples from the stories collected that are illustrative of the personal dimensions represented in the sample collection analysed rather than representative of the immigration history in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".