THE ONE MILLION TUMBLER MOVEMENT: STATE CIVIL SERVANTS’ PERCEPTION ON TUMBLER USE AND PLASTIC WASTE REDUCTION
Bibliographic record
Abstract
One of the Indonesian government's steps to overcome the plastic waste problem is by initiating The One Million Tumbler Movement campaign. Civil Servants as government agents must have good knowledge and apply this policy in their lives and their environment. This is a case study approach through in-depth interviews, field observation, and literature study. The informant of this study was The Center for Standardization of Disaster and Climate Change Instruments (PUSAT-SIKBPI) civil servants with structural and functional career backgrounds. The result showed that the civil servants have knowledge about plastic waste reduction and have a supportive perception of tumbler use. The Center for Standardization of Disaster and Climate Change Instruments’ management supports the tumbler's use as a new habit related to The One Million Tumbler Movement campaign. The family values on environment characterize the tumbler and plastic bag uses in general. However, although civil servants’ environmental awareness has formed, the Covid-19 pandemic has made plastic consumption unavoidable. Support from the environment (The management and the family environment) is essential to help civil servants continue habitual implementation of tumbler use and the plastic awareness movement in general.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".