Delving into the Mysteries of Dickinson's "'Nature' Is What We See"/ETUDES APPROFONDIES SUR LES MYSTÈRES DE "'NATURE' EST CE QUE NOUS VOYONS" DE DICKINSON
Bibliographic record
Abstract
This article is an attempt to decipher the meaning of Emily Dickinson’s poem 668 “‘Nature’ is what we see” by observing the different mentioned objects (used here to refer to animals, insects, natural phenomena and natural objects) and discovering patterns between the objects in each group as well as patterns that exist between the objects in each group and those in other groups to prove that the choice of these objects was not arbitrary but rather deliberate. Dickinson, as proven in this reading of the poem, tries to reflect the diversity of nature through her choice of the objects she lists in her poem.Keywords: Dickinson the would-be scientist; Natural Diversity; Poem 668Resume: Cet article est une tentative de dechiffrer le sens du poeme 668 d'Emily Dickinson - ‘Nature’ est ce que nous voyons, en observant les differents objets mentionnes (utilises ici pour designer les animaux, les insectes, les phenomenes naturels et les objets naturels) et decouvrant les modeles entre les objets dans chaque groupe, et ceux qui existent dans d'autres groupes afin de prouver que le choix de ces objets n'est pas arbitraire, mais plutot delibere. Dickinson, comme le demontre dans cette lecture du poeme, tente de refleter la diversite de la nature a travers son choix des objets qu'elle enumere dans son poeme. Mots-cles: Dickinson le soi-disant scientifique; diversite naturelle; Poeme 668
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".