Growing our Vision Together: A Sustainability Community within the American Library Association
Bibliographic record
Abstract
A rich and colorful tapestry of innovative library practices, services, and engagement emerged in response to the economic, social and environmental dynamics of the 21st century, not least of which the explosion of technology, economic crises, and a growing environmental imperative driven by climate instability. In a world struggling for sustainability, libraries continue to critically evolve in order to celebrate their communities' successes and support them through hardships. Library associations bring together professionals to co-create solutions, share expertise, and bolster resilience through learning and community building. This poster reports on the formation of the American Library Association (ALA) Sustainability Round Table (SustainRT) in 2013, the result of an urgent call to action for a unified effort to address the new millennium's environmental, economic and social sustainability challenges within the library profession in the United States and Canada. This poster identifies the technologies, processes, roles and other factors that led to the founding of SustainRT, as well as providing a vision for the future based on its participatory and inclusive structure. This story offers a practical model, including tools and strategies, for others seeking to engage in dialog and collaboration within the library profession.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.058 | 0.017 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".