Feeding Your Fire (Without Burning Out)
Bibliographic record
Abstract
I was on a call to help a dog that was hit by a car, and I showed up and it was a sheltie," he says."And I've owned shelties all my life, and immediately I saw my dog on the side of the road, dead."Abi-Hassan, executive director of the Halifax Humane Society in Daytona Beach, Fla., who teaches workshops on stress management and compassion fatigue issues, says it was one of many moments that showed how much he and other animal welfare workers need to develop the emotional skills to survive."It really took me back and made me realize that what I need to take care of is me," he says, "because if I don't take care of myself, I'm not going to be around to take care of these animals."It makes sense, and yet animals need so much, and so many of them are suffering.And whether you work hands-on in the field or in the shelter, in your home as a foster caregiver, or in an office on policy issues that can help animals on a national level, what needs to be done can seem endless.In a recent employee feedback session at The HSUS, staffers were asked to share ideas about how to make their work environment better.A huge sheet of paper was pinned to a wall, and employees sounded off about ongoing challenges, offering suggestions and commenting on each other's thoughts.In one spot, someone wrote: "People feel like they have to be on the job 24/7/365!There needs to be more work-life balance."Next to that, someone had retorted: "Animal cruelty doesn't end at 5 o'clock."Others had chimed in on each side, drawing arrows and plus signs and saying, "Exactly!"It's a debate many animal welfare advocates have regularly, among each other and within themselves.The suffering we confront is so great it seems to demand all of our hearts.Yet if we give all of our hearts, what's left of us to keep giving?Advocates working for major societal change frequently wrestle with the implications of psychiatrist and Holocaust survivor Viktor Frankl's credo: What is to give light must endure burning.Many accept that sacrifice to a greater cause.But how can we keep our fires lit without burning out, take the time to refuel, and find the "kindling" we need to stay healthy?In the standoff between I-have-to-help-more-animals and I-need-some-space-to-breathe, some people never figure out the answer.Some figure it out-and change jobs.And some manage to find a balance that works.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.279 | 0.219 |
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".