Bibliographic record
Abstract
Position taking is an important legislative area that scholars have investigated extensively. Since one of the main roles of the opposition is to present an alternative to the government, the question is, how does the opposition establish its positions? To address this question, we analyze the use of One-Minute Speeches (OMSs) by opposition members in the Israeli parliament (the Knesset) during 2000-2013. There were four Knesset terms during these years, so we have the opportunity to study the opposition’s behavior over a period of time. We decided to analyze OMSs because they are considered an easy tool to use and as such can be considered a weapon of the weak. The study uses mixed research methods, beginning with a statistical analysis (both at the legislator level and at the OMS level) and continuing with a content analysis of the speeches and the interviews conducted with members and leaders of the opposition. The statistical analysis shows that opposition members use OMSs more extensively than coalition members. Among the opposition members, we also found different behavior patterns based on nationality and seniority. In addition, the qualitative analysis of both the OMSs and the interviews shows that opposition members are active in two ways. First, they react to government-initiated proposals. Second, they raise topics for the Knesset’s agenda, a move that the coalition generally does not appreciate. Third, members of the opposition consider OMSs an effective tool in that it allows them to create a relevant debate on current issues. Finally, ministers and other MKs often respond to the opposition’s OMSs that are controversial and provoke debate. Thus, we conclude that the OMS is a weapon of the weak.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".