Bibliographic record
Abstract
column was: “From a leader’s perspective, what will change in nursing research over the next five years?” The question carries with it a number of assumptions – that I in some way fulfill the requirement for leadership, that I know with some precision where nursing research is in 2003 and that I can foretell the (albeit short-term) future. Feeling on shaky ground on at least two of these assumptions, I turned to others – in and outside the nursing profession, well known and not – for direction. My colleagues foretold glory days, gnashed teeth, told me what should be, wrung hands and presaged a barren, post-apocalyptic landscape. A few ignored me. With little recourse, I resolved to create a new question, one I could answer. My complaints about the original question were numerous: it was the wrong question; it was not nearly a long enough period of time in which to see observable change; it wasn’t an important enough question; and why wasn’t I given the better question: What should change in five years? I did not arrive at a newer and better question, and so found myself alone again ... the cursor blinking unremittingly. Sometime during the last procrastinating trip out to prune the roses, I decided there was nothing to do but answer the question based on my travels, recently attended research events, the spate of reports written lately on nurses and conversations with countless nurses in the past 25 years. I do not know with objective precision the state of nursing research in Canada today, nor can I predict the future, even in the short term. But here are my best efforts as a successful career scientist in this country, working in a field that 10 years ago was proclaimed by some as nonviable. I will make my predictions after a quick glance at four recent events. Perilous Journey: Canadian Nursing Research in 2009
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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.047 | 0.012 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".