Bibliographic record
Abstract
One of the remarkable things I notice when I go to Modelling & Simulation (M&S) forums such as this one is when three-quarters of the room is silent and the remaining quarter doing all the talking already knows most of the answers anyway. Although the retention of vast corporate knowledge is certainly a plus in our community, it does not bode well for our future when the walls we place between ourselves and key leaders are self-imposed. The purpose of this lecture is to encourage the three-quarters to meet the few of us halfway by gaining a measure of self-confidence through a professional M&S reading programme. Most of those present are not here because of a keen desire to programme; although a few can. These lecture series are conducted to empower the implementer. If you are here to gather information in support of a senior General and are unable to articulate the M&S world view, we are doing you and ourselves a disservice. That is why I have compiled a very innocent list of answers to common questions and included the supported web sites so you can have your own on-line M&S library. Whether we realize or not, those of you looking for an answer need something more instantaneous than reading an entire book. What I have done is by no means exclusive to the selection placed in your hands, but it is indeed a start.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.759 | 0.680 |
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".