Bibliographic record
Abstract
"What are you going to do next?" is a common question often asked of a student who has recently graduated with either an MSc or PhD degree. We should not be surprised to hear the answer "I do not know yet." I have talked with many poultry science graduate students who usually start thinking about their future careers a few months before defending their thesis. I personally believe that nothing happens overnight in this world (excluding political-related issues), so we as graduate students need to have a comprehensible and pragmatic strategy when it comes to answering the question "What to do next?" This paper is not about how graduate students can find a job because there are numerous sources of information that are readily available elsewhere. One of the key messages of this paper is that networking is of paramount importance when it comes to moving in the right direction after graduation. Consequences of any decision made at this stage will often have a far-reaching unseen influence on us for many years into the future. I am also fully aware that there are many things over which we do not have any control, but as graduate students, are we doing our best to prepare ourselves for the real world?
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 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".