Bibliographic record
Abstract
As individuals are increasingly being called to act in environmentally-conscious ways, they will seek out any and all resources which might help to inform their actions. Unfortunately, a 2002 OECD report indicated that there seemed to be declining trust of environmental information sources and increasing confusion about which actions could be most beneficial. Environmental Software Systems can provide relief in this context, if the software design is user-centered. The very specialized nature of much environmental software may discourage these design practices, but this is a false economy. Instead of software that is based on technical models developed by environmental scientists, consider that which can acquire and adapt to changing end-user models of the particular domain. Such adaptation would certainly benefit users, but research could also benefit considerably from data that could prioritize actions of the environmental scientists, from analysis to education. This shift in emphasis agrees with recent trends toward personalization and democratization of software system functionality. If the number of people who could meaningfully explore a model of a particular ecosystem could increase thousand-fold, there could be considerable benefit realized in the level of discourse on environmental issues pertaining to that ecosystem. Such an increased usage would require the removal of barriers for direct user access to the software systems in order to create satisfying user experiences. For the user to be satisfied when confronting a large and complex information space, he or she must not be overwhelmed but able to easily specify and locate that which is of interest. The theoretical basis for such an approach is presented, along with some evidence thus far collected. Opportunities for improvement are also discussed.
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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 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".