Bibliographic record
Abstract
There are a few things that you need to know to live well in rural Canada. These are true for all adults but becomes even more important as you age. First, you absolutely need a reliable vehicle. You need to know what to do and be prepared for emergencies, such as power outages, snowstorms, ice buildup, and vehicle and machine breakdowns. Luckily, in a rural area, you can depend on your neighbours for support. And in turn, you must be willing to share and offer up your own snowblower, lawn mower, rhubarb, or dishes for potlucks. You need to remember that everyone is related by marriage or were classmates (so keep your negative comments to yourself). Make sure to wave to everyone who drives by (two fingers off the steering wheel is the usual acknowledgement). Rural places are small, so you know the foibles of others and can (usually) deal with them. But because they are small, there is always anxiety that healthcare practitioners will leave the community. The small size of rural communities can also lead to social isolation, especially if you are new to the area. The saying goes that ‘residents are friendly but not welcoming’. Most people have their own social network and don’t need to include you. Newcomers have to be forward to enter into existing groups; they need to be assertive in making friends, offering to join or create a group, or have children to ‘break the ice’ for them. The good news is that these networks are incredibly strong in rural areas.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.005 |
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".