docs: document speciesist bias in word vectors and NLP models

## Proposal

Document speciesist bias as a recognized form of bias in spaCy's documentation, particularly in sections about word vectors and model evaluation.

## Context

Peer-reviewed research has demonstrated that word embeddings and NLP models — the foundation of spaCy's pipeline — encode speciesist associations:

- **Takeshita et al. (2022).** "Speciesist language and nonhuman animal bias in English masked language models." *Information Processing & Management*. Found BERT, DistilBERT, RoBERTa, and ALBERT all associate harmful words with nonhuman animals.
- **Hagendorff et al. (2023).** "Speciesist bias in AI." *AI and Ethics*. Documented speciesist bias across image recognition, word embeddings, and language models.
- **Leach et al. (2023).** Large-scale word embedding analysis establishing anthropocentric speciesism in everyday language. *British Journal of Social Psychology*.

## Relevance to spaCy

- **Word vectors** (en_core_web_md, en_core_web_lg): Pre-trained vectors inherit speciesist associations from training corpora.
- **NER**: Named entity models trained on biased corpora may treat animal-related entities differently
- **Text classification**: Downstream models built on spaCy inherit these biases
- **Sentiment**: spaCy-based sentiment pipelines reflect speciesist patterns in training data

## Suggested Change

A brief note in the word vectors or model evaluation documentation acknowledging speciesist bias as a documented phenomenon, with citations. This aligns with spaCy's existing documentation on model limitations and biases.