Bibliographic record
Abstract
This is a book about recovery. Not recovery from drugs, alcohol, or surgery, but recovery from the numerous and relentless demands we face in handling our everyday obligations. These demands take a toll on us. Regardless of whether they come from paid employment, caring for young children, looking after elderly parents, or trying to get through graduate school, our daily obligations weigh heavily on us. They deplete our energy. They drain us of motivation. They leave us feeling weary and exhausted. If you tend to feel worn out and want to know how to replenish yourself, this book is for you.We should be able to recover from our daily obligations during our downtime. But many of us don’t. In this book we will explain why downtime is inadequate for helping us recharge our batteries, and present you with an effective alternative. Recent scientific developments from around the globe have shed light on the processes that reverse the draining effects of our obligations and help us successfully recover in our leisure time. Not only that, research also reveals that when effective recovery occurs it not only recharges our batteries, but makes us feel happier, makes us healthier, and makes us better at handling the demands that drained us in the first place. We call this boosting to reflect the multi-pronged benefits of successful recovery. In this book we draw on the most cutting-edge science to explain how to transform our ineffective downtime into valuable uptime. Uptime is the time away from our obligations that successfully satisfies the factors that lead us to feel replenished, recharged, recovered, and gives us a boost.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.067 |
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".