Alaska Snapshot: What's Happened to the Alaska Economy Since Oil Prices Dropped?
Bibliographic record
Abstract
North Slope oil has paid for most of Alaska state government—and \nindirectly, a big share of local government—since the 1980s. It’s also been \nthe backbone for much of Alaska’s economic growth over time. But today, \na combination of declining oil production and sharply lower oil prices has \nleft the state budget billions of dollars in the red and is reverberating \nthroughout the economy. \nHow has the big drop in oil prices affected the Alaska economy so far? \nThis paper looks at that question, using changes in the number of jobs— \nstatewide, and also by census area and sector—as a gauge. We look \nspecifically at the period from March 2014, when oil prices were over $100 \na barrel, through March 2016, when prices had dropped below $40. \nWe use that period because right now reliable employment data are \nonly available through the first quarter of 2016. Also, this is a broad look \nat job changes, not a detailed analysis of all the specific changes we found.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".