Bibliographic record
Abstract
What we see is partially dependent on what we are shown. As communicators, we have a duty to inform and educate and lead. As environmental communicators we have the privilege of explaining how the various parts of our natural world work, individually, in unison, and in relationship to people. By examining two specific areas of growing global concerns, this paper provides an analytic tool and starts a discussion as to what should be guiding decisions concerning major environmental questions. The first growing global concern discussed is tailings ponds in Northern Alberta’s oil sands. The second is the large bodies of air pollution in Asia. In both cases, (Good) short term decisions that benefit a few have led to large environmental concerns. Should humanity be worried about our future? Could (Right) long-term, sustainable, and inclusive decisions lead to more manageable environmental challenges? To be a communicator in the real world it is important to know and differentiate between the Good and the Right. Good and Right communications in environmental issues support daily or frequent acts concerning any or all of three critical areas: sustainability, conservation, and climate change. Questions are addressed. Where are people now with respect to environment, how did we get here, and what are the pros and cons of changing from Good to Right solutions? By looking at one individual’s choice, readers see that Good and Right decisions do not have to be mutually exclusive.
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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".