Bibliographic record
Abstract
Only July 11, 2012, the day that hundreds of scientists marched on Parliament Hill in Ottawa to protest cuts to scientific research by the federal government, a reporter asked me why the public should care about science.I was dumbfounded, largely because I had never really considered that a question that needed answering.I had no idea there were people who did not inherently understand why science is important in our everyday lives.My thinking was naïve, and ever since I have been on a journey to more fully understand -and communicate -the way science influences our lives.As a result, when I was approached to act as guest editor for this edition of Scholarly and Research Communication (SRC), I was eager to help assemble an issue that would explore the importance of science in our lives.One of the main themes of this issue's articles is the question: "What does science mean to me?"In my own exploration of that question, I realize that I never made the choice to be a scientist.is is further illustrated by my experience with my young children.I now understand that we are born with an inherent curiosity that is the basis of science.As Carl Sagan said: "Every kid starts out as a natural-born scientist, and then we beat it out of them." I was lucky in my upbringing, an interest in science was fostered.I grew up in a family of thinkers, with a family history of successful scientists and a couple of world-renowned biogeochemists to boot.at is one of the reasons why I was so surprised that anyone would actually question the value of science and equally surprised that I struggled with an answer.
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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.029 | 0.022 |
| Insufficient payload (model declined to judge) | 0.041 | 0.021 |
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".