MENGELOLA KONFLIK PEMANFAATAN RUANG BERBASIS NILAI NILAI LOKAL RUANG KOTA KORIDOR JALAN MALIOBORO - KOTA JOGJAKARTA
Bibliographic record
Abstract
Corridor of Malioboro Street has the potential to spatial utilization conflict. Spatial agents think and attempt to build a strategy in order to remain and be able to utilize spaces for activities, but by reducing risks of conflicts as minimum as possible. The question is how is the strategy to manage spatial utilization conflict that they build together? This research applied the approach of phenomenology naturalistic, the research finding shows that the spatial consensus concept built in spatial utilization. The spatial consensus built based on compromises and negotiation has been built in a long period of time. The agreement based on their own initiatives, the representative system was through the community and association boards, and using the third party as facilitator. The substance contained in spatial consensus concept is building the communication to deal with the utilization of space collectively and in the same or different period of time by still prioritizing the principle of togetherness, tolerance, and mutual understanding not to harm others and then called as local values used as the norms and institution to agree with collectively.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".