12 The myth of hard & soft sciences
12.1 Key Message
đź‘Ť Realization 2
Hard & Soft are also used as inadequate, hierarchical adjectives for Scientific Disciplines
12.2 Hard & Soft also Describe Scientific Disciplines
- Returning to this use of hard & soft will help us to appreciate what really distinguishes these skills and disciplines
12.3 Value Perception has Serious Consequences for Research
A lower perceived value results in poorly supported research areas, both financially, institutionally and publicly.
Ethics, privacy, transparency, explainability, reproducibility and human rights have potentially all suffered as a result.
12.4 Hard & Soft describe a binary value hierarchy of scientific disciplines
- Similar to hierarchy in skills:
- Level 1: STEM on top, everything else on the bottom
- Level 2: within STEM, subtler hierarchies exists
This is less an appreciation of distinguishing features and more a tallying of “pureness”, again reinforcing masculine traits of dominance and elitist attitudes of purity and exclusivity, even among STEM fields.
12.5 Hard & Soft Reinforce a Misogynistic STEM Culture that Diminishes Soft Traits (again)
- A long history of systematic sexism in STEM fields, when women were explicitly excluded from the scientific endeavour, is casually, consistently and persistently reinforced. e.g.:
- Communication seen as a female pursuit.
- The late integration of biology as a real science but acceptably practiced by women outside of academia (e.g. Beatrice Potter).
12.6 Metrics used to define Hard & Soft sciences are outdated oversimplifications
- The metrics which distinguish hard and soft scientific disciplines are provided in the following table and are generally taken as binary.
- This is the same false dichotomy that happens in the examination of skills.
| Metric | Hard Science | Soft Science |
|---|---|---|
| Falsifiable hypothesis | âś“ | âś— |
| Controlled experiments | âś“ | âś— |
| Quantifable data | âś“ | âś— |
| Mathematical models | âś“ | âś— |
| Objectivity | âś“ | âś— |
| High Accuracy | âś“ | âś— |
| High level of consensus | âś“ | âś— |
| Applied/practical | âś“ | âś— |
| Explanatory success | âś“ | âś— |
| Cumulativeness | âś“ | âś— |
| Reproducible and replicable | âś“ | âś— |
| Scientific method | âś“ | âś— |
12.7 Hard & Soft scientific disciplines rarely even still exist
- Perhaps these metrics were clear, diagnostic binary metrics at some point in the past.
- In the current research environment, supported by modern technology and the scale it facilitates, it seems unlikely that many disciplines can still be characterised so simply as “hard” or “soft”.
- Quantitative methods, typically associated with STEM are routinely used in nonSTEM fields, e.g. digital humanities, NLP, experimentation, the scientific method, etc.
- Practices which would have once been frowned upon in STEM are now routine:
- Gathering data and then developing a testable hypothesis,
- Machine Learning & Deep Learning models which are so complex that they need further study just to understand
- The introduction or both randomness (dropout deep learning) and stochastic processes
- In short, there is a dichotomy that can be applied to both “skills” & “scientific disciplines”, but it’s not between “hard” & “soft”. So what it is?