Bibliographic record
Abstract
Methodology"Regardless of how the first-hand experiences are used in the text, we can assume that the arrival itself is an important experience.Any ethnographer would probably agree that first encounters generate personal alienation and a sense of extreme relativism that forever marks off the 'field' .First experiences belong to an experiential space that cannot be done away with literary criticism" (Hervik 1994: 60).During the fall of 2016 and the winter of 2019, I conducted fieldwork in Montreal and Jamaica in several periods and trips from one place to another.I first came to Montreal in the summer of 2016 to gain orientation knowledge in the city and to explore the local setting.However, I quickly realized that I had chosen the wrong part of town since I stayed in a designated Francophone area at the house of a dear colleague from the Université de Montréal.Before arriving in Montreal for the first time, I was unaware of the linguistic divide of the city that has effects on residential as well as on livelihood patterns.For my second trip to Montreal in the fall and winter months of 2016, I therefore chose a more central area close to Mount Royal, from which I could easily travel to the western neighbourhoods of the city that are predominantly Anglophone.An essential part of my field research preparation and orientation knowledge acquisition was, what Hine calls, "virtual" ethnography (Hine 2000).As practical examples of cross-cultural exchange, various social media platforms, including Jamaican-Montreal-based Facebook groups were very useful in exploring the 'virtual' field.Here, those interested will find help with migration/ integration related questions, visa requirements/ procedures, or just practical information about grocery shops, Jamaican-owned businesses, and restaurants in Montreal.In addition, Jamaican musical events, radio stations, and upcoming Reggae/ Dancehall parties in town are shared on various social media platforms.Besides, users frequently add links to YouTube channels or video platforms with Jamaican news, music videos, comedy, or commercials.Information about the local Jamaican association in Montreal and its events are circulated and are part of a conversation among the chat participants.These technological spaces are anticipated and used daily.Further,
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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.261 | 0.067 |
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".