Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We compute classical real GDP business cycles and growth cycles, contrast classical recessions with ‘technical ’ recessions, and assess the sensitivity of our peaks and troughs to data revisions. Calling a technical recession after two successive quarters of negative growth can provide conditionally useful information. However, it can also signal beginning and end points for a recession that are somewhat different from those computed by our Bry and Boschan algorithm. Expansion and contraction phases of classical real GDP and employment cycles have, on average, had an 89 % association, but individual cycle circumstances should additionally be assessed. New Zealand’s average pattern of recovery has differed from that for U.S. NBER cycles, but their most recent recession and recovery paths have been unusually similar. We also assess whether strength of recovery can be explained by length, depth or severity of previous recessions. From our classical real GDP turning points, New Zealand’s most recent recession commenced with the March 2008 quarter and ended with the June 2009 quarter. The duration of this six-quarter recession has been somewhat longer than the average
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it