Bringing Animal Voices to the Table: Exploring Intuitive Interspecies Communication as a Method for Conservation and Human-Animal Coexistence
Bibliographic record
Abstract
Despite growing acknowledgement of animal ‘agency’ and ‘consciousness’, more-than-human animals (hereafter ‘animals’) continue to be excluded from decision-making in relevant issues. The barrier that is frequently identified to truly bring animal voices into research, is their inability to speak human language, and human’s limited capacities to understand theirs. Used by animal communicators (ACs) to engage in two-way communications with animals, the method of intuitive interspecies communication (IIC) is a possible response to this barrier. Using exploratory case studies and reflexive thematic analysis, this work provides detailed, well-documented accounts of cooperative work between ACs, animals, and third-party human stakeholders in issues related to conservation and human-animal coexistence. Three cases were documented in total: one individual and two nested, which included individual mini cases within the larger case. The cases showed IIC being used to achieve greater mutual understanding between humans and animals, as well as animal engagement with prospective interventions that will impact their wellbeing. IIC also eliminated guesswork in project planning; by engaging the animals, they can be asked for input in various conservation and human-animal coexistence issues that arise. Reflexive thematic analysis illustrated the roles of all animal and human stakeholders, the ethical orientation of the humans involved, the approaches and strategies of the ACs, and the outcomes of IIC-facilitated human-animal engagement. Ultimately, study findings suggest that ACs can work as “bridges” to facilitate animal engagement in conservation and human-animal coexistence issues that affect them.
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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 | 0.052 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".