Animals as Objects? / Tiere als Objekte?
Bibliographic record
Abstract
Animals as Objects? is a digital publication by the research project "Animals as Objects. Zoological Gardens and Natural History Museum Berlin, 1810 to 2020". The website animalsasobjects.org makes our research results on selected objects, histories, and species of animals available to German- and English-speaking audiences. We share historical, archival, and ethnographic answers to the question: How have animals been turned into natural history objects and data? Relational databases, the very databases that are often used to record and manage natural history collections, inspired the conceptual and visual design. There are three different types of articles: 1. Materials focus on specific photos, objects, documents, events, etc. 2. Stories answer big and small research questions through materials and other sources. 3. Themes address the bigger picture that links various stories and materials. They are interconnected in many different and sometimes surprising ways, emerging from the authors’ situatedness (in a discipline, institution, subject position) and interests. Tiere als Objekte? ist eine digitale Publikation des Forschungsprojektes "Tiere als Objekte. Zoologische Gärten und Naturkundemuseum in Berlin, 1810 bis 2020". Die Website animalsasobjects.org macht unsere Forschungsergebnisse zu ausgewählten Objekten, Geschichten und Tierarten für eine deutsch- und englischsprachige Öffentlichkeit zugänglich. Wir bieten historische, archivarische und ethnographische Antworten auf die Frage an, wie Tiere zu naturhistorischen Objekten und Daten gemacht wurden. Relationale Datenbanken, genau die Datenbanken, die häufig zur Erfassung und Verwaltung von naturkundlichen Sammlungen verwendet werden, dienten als Ausgangspunkt für die konzeptionelle und visuelle Gestaltung. Es gibt drei verschiedene Arten von Artikeln: 1. Materien legen den Fokus auf spezifische Fotos, Objekte, Dokumente, Ereignisse, usw. 2. Storys liefern mithilfe von Materien und anderen Quellen Antworten auf kleine und große Forschungsfragen. 3. Themen öffnen den Blick für den thematischen Rahmen und bringen die vielen verschiedenen Storys und Materien zusammen. Sie sind in vielfältiger und manchmal überraschender Weise miteinander verbunden und ergeben sich aus der Situiertheit (in einer Disziplin, Institution, Fachposition) und den Interessen der Autor*innen.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".