Traditionally, research is viewed as an activity best kept isolated from the wider socio-economic arena, with engagement between the research community and all other groups confined to the limited circumstances in which a discovery has been made and a purpose for its use has been identified.
Ideas have formed based on this view that are developed into policies that have a reinforcing effect on constituents in the research economy as well as those who may otherwise consider engaging with it.
A key step toward reshaping the research supply chain involves busting some of these myths, so as to shape opportunities for improvement and address challenges that stand in the way of research making a socio-economic impact.
This paper addresses some of the recurrent myths that persist in the sector.
There is a linear progression from hypothesis to impact
The translation pipeline from hypothesis to commercial outcome is or can be shaped into a linear progression from research to development to commercialisation.
The idea that research and development should be conflated to interconnected progressive steps appeals to those funding it. Very often, promising developments evolve from a strong research base but are not linked in this step-ladder form.
It is possible that a well crafted research proposal can narrowly scope a field of exploration, and in doing so, improve the ability to quantify potential outcomes, however that is a long way removed from the level of certainty that policy-makers hope to use for the purpose of forecasting, hedging and disseminating research risk in the wider economy.
Incrementalism
Well designed research neatly takes established theory and incrementally expands on it by reducing or removing approximations or assumptions.
50 years ago, Kuhn observed that scientific practice alternates between periods of normal science and revolutionary science. During periods of normalcy, scientists tend to subscribe to a large body of interconnecting knowledge, methods, and assumptions which make up the reigning paradigm.
The discovery of “anomalies” during revolutions in science leads to new paradigms. New paradigms then ask new questions of old data, move beyond the mere “puzzle-solving” of the previous paradigm, change the rules of the game and the “map” directing new research. This observation seems as relevant today as it was decades ago.
GoogleX is an example of a corporate pursuit in paradigm shifting discovery.
Research is funded from licensing or sales revenues
Registrable intellectual property (patents) are the measure by which successful research is quantified and that licensing by those who create it is the uniform business model that is a sustainable or sensible way to deploy those discoveries and fund a new cycle of research.
In the academic research sector, the significant majority of technology transfer offices (who bear responsibility for the management of licensing activity) operate at a loss.
In industry, R&D budgets traditionally compete with shareholders for distributable reserves to fund research programs. Increasing use of alternative financing techniques are being used as a means of raising research capital.
Sector based trends indicate the impact of short-term decision making and shareholder priority. For example, the rise of the PLIPO (productless IPO) shows that impatient capital and the financialisation of the US biopharmaceutical industry is driving short-term R&D decision making on a wholesale level. While the surge in capital is allowing the sector to raise funding for risky research, commentators acknowledge that these waves tend to end abruptly and may be driven more by M&A potential on the target than scientific consensus around the research pipeline.
The publication mantra
The lore (or law) that peer review of academic publication is the measure of success (and means of survival) in an academic career.
Whilst the statistics indicate that the probability that an experiment might identify scientific discovery consistent with a hypothesis is around 20%, almost all studies conclude that a significant discovery had been made. It’s not difficult to understand why.
The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science.
Publish -v- patent
The old school system for sharing new knowledge is premised on the freely available publication of research funded by the taxpayer (paywalls aside). Its intangible nature means that use by one person does not prevent use by another. It is freely transferable and comes at no direct cost to those seeking to use it (beyond the taxes used to fund its creation).
In recognition of the value of proprietary knowledge, the intellectual property rights system of patenting discoveries to allow commercial reward for the investment in knowledge, served a role in response to the open market failure.
The pendulum swung from publish to patent. Policy makers hop between these binary options. Each of these positions affords them political cover to criticise researchers as being ignorant of their station while at the same time, avoiding responsibility for addressing the substantive underlying issues.
The move to adopt a hybrid of citation and patent metrics provides political cover for those who seek to disclaim responsibility for choosing between these poor choices, whilst continuing to use them as safe and easily quantifiable metrics as a basis for managing the research budget.
The widget fallacy
Knowledge is perfectly formed by a researcher, before being marketed to an identifiable target market (within the period before journal publication), for a known application, and that no further research will be required in order to put it to use.
The proprietary knowledge transfer model falsely assumes that:
- no complementary knowledge will be required to use the research;
- interaction between users and creators is inconsequential to the research; and
- the bundled up package that a researcher produces as output will be a perfectly complete information set, capable of being delivered as a widget that can be plugged in and switched on by a user who themselves does not have or require specialist knowledge.
Those active in the research base are often practising in galaxies far removed from those operating in the development of related discoveries. They speak different languages, are motivated by different incentives and are pursuing different objectives. Successful adoption of a discovery often requires adjacent skills on the part of industry.
Those who participate in these pipelines have improved chances of successful research commercialisation if they are involved in the research design, conduct and translation strategy. This engagement opportunity raises new governance questions that require research integrity issues to be addressed with fundamental priority.
By challenging convention, we can restate the challenges that need to be addressed in order to better identify and support research through the translation pipeline.
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