Social Weather Stations Survey: 2015 Third Quarter
Bibliographic record
Abstract
Quality of life trend (1); personal optimism/pessimism (1); optimism/pessimism on the economy (1); following news events (15); awareness and performance rating of President Benigno Aquino III (Noynoy Aquino) (1); best leader to succeed President Noynoy Aquino in 2016 (1); voter preferences for 2016 elections (30); President Noynoy Aquino (1); state of the nation address (3); true state of the nation address (2); awareness/performance ratings of government officials (17); best neophyte senator (2); Senator Bam Aquino (3); awareness/performance ratings of government institutions (7); performance ratings of the present national administration (23); issues that the next president should focus on (1); issues in agriculture that the government should focus on (1); qualities and attributes of a president (3); issues that would make one vote for a candidate (10); methods used by candidates to win (2); endorsement (6); anti-dynasty law (2); Bangsamoro Basic Law (8); peace process with the government and Moro Islamic Liberation Front (4); peace process with communist rebels (3); awareness and trust ratings of selected personalities (11); awareness and trust ratings of institutions (6); awareness and trust ratings of countries (7); foreign relations (2); consumer protection (2); crime victimization (6); Maguindanao massacre (2); agree/disagree statements (4); happiness (1); life satisfaction (1).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.107 |
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; both teacher heads agree on what is shown here.
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".