Power and Responsibility
Bibliographic record
Abstract
Agencies enact a new, stepped-implementation strategy in their solicitation, review, selection, and award processes.o Next issuance of solicitations require proposals to include the proposing institution's policies and practices in Diversity, Equity, Inclusion, and Access (DEIA), and related information/data describing success or failure on these fronts.o Subsequent issuances require proposals to include this same information, and will supply criteria (developed through external expertise) against which it will be evaluated.o Special review panels are convened to evaluate this information separately from those that review the proposed science and technology.o As part of the awarding process, negotiations can take place between agency and institution toward procurement of the proposed work and any needed policy remediation; proposals can also be rejected on the basis of low DEIA scores.• Through this process, agencies gain knowledge and wisdom to evolve their own DEIA policies and practices toward the development of a safe, diverse, anti-racist workplace. Summary of Impact:By 2032, it is imagined that this new process will ensure the science and technology that agencies procure and support are able to develop in institutional environments reflective of excellence in anti-racist policy and practice.Agencies will no longer be complicit in the perpetuation of the policies and practices of any institution to which they transfer funds that actively or passively allow racism, sexism, homophobia, transphobia, ableism, classism, colonialism, and other insidious thought processes, value systems, and systemic or individual behaviors to persist.With the full participation going forward of people of all colors, genders, abilities, walks of life, and ways of loving and praying in a working environment where they can bring their whole selves and be free from harm, agency-funded achievements will authentically represent the humanity that planetary exploration aspires to serve.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".