Bibliographic record
Abstract
We have used the terms “process” and “system” several times already in the preceding chapters. To many they are synonymous, but this is not true. A process is defined as a whole series of continuous actions or tasks, or a method by which something is done. A system is defined as a group of objects related or interacting so as to form a unity, or a methodically arranged set of ideas, principles, methods, procedures, etc. It can therefore be seen that a system is on a more macro scale than a process and, indeed, typically comprises a collection of processes, some of which might occur sequentially while others might occur simultaneously or in parallel with one or more other processes. At the most basic level, a process can be defined as a single, simple sequence, as illustrated in Figure 5.1. Systems analysis A system typically comprises several processes, some of which might run in parallel, but many of which usually operate serially or in sequence, i.e. the output of one is an input to the next. Systems analysis can be defined as the diagnosis, formulation and solution of problems which arise from the complex forms of interaction in any system (e.g. from computer hardware to corporations) that exist or are conceived to accomplish one or more specific objectives.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".