Bibliographic record
Abstract
Okay, so we're going to go ahead and start, and I'll have you tell me your name and then a little bit about how you got started in farming.Sarah: Okay, my name is Sarah Robinson, and I started farming with my husband ten years ago.We kind of were both working and miserable [laugh].Making good money and had decent careers, but we were both racing each other home to water our small patch of beets in the backyard.So, I think we realized we wanted a change, and we went on Landlink and found a quarter acre and started that year growing as much as we could and taking it to the farmer's market.And bootstrapping our way ever since. J: Can you give me an example of what you mean by miserable?S: [laugh] Yeah, my husband was working for a seismological labs, so he was travelling all the time.So, he'd be gone and he got to go to these really great places nobody else gets to go -Antarctica, a few times, Papua New Guinea -so really cool job, but we didn't get to spend time with each other as much.He's be gone all the time.And then when he was back he was just at a desk, you know?And then I was working for various nonprofits and just struggling to make a change, right?Just feeling like every day we're just going to some job and nothing is happening; we're not making a difference.So, as corny as it sounds, making, you know, the change we wanted to see in the world was going to be through farming.J: And you mentioned, was it, Landlink?Yeah, what is that?S: So, it was defunct for a while, but I think they're back online now.And it's just a website landlinknm.org,I believe.And they match folks, particularly in the valley, here where we are in the north valley, that have land but don't necessarily want to work it.But don't want to lose it either and want to maintain their agricultural tax benefit.And then they match folks with people like us who don't have the fund to purchase land in this area but do want to practice agriculture on it.And so that's how we found our first, wonderful family that helped us out and gave us a lease on their quarter acre to start.J: And how did you hear about them?S: Good question.I had recently graduated from UNM and was dabbling in what was beginnings of their sustainability program.And so, probably there?I'm
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".