Best Practice Science or Best Practice Spin?

How “best practice science” became a shield for opaque decisions, institutional self-protection, and the hollowing out of Australia’s domestic seafood industry

For years, commercial fishers have been told that the rules, closures, restrictions and management changes affecting their livelihoods are grounded in best practice science. The phrase sounds reassuring. It suggests rigour, neutrality and public benefit. It carries the tone of something beyond politics, beyond ideology, beyond dispute. Once those words are invoked, the public is expected to relax and trust that the experts have it in hand.

But that is precisely why the phrase deserves closer scrutiny.

Science should be a method of inquiry, not a branding exercise. It should be tested against outcomes, challenged by reality, and open to revision when the facts do not line up with the theory. Yet in modern fisheries governance, and in environmental governance more broadly, best practice science is often used less as a transparent standard and more as a reputational shield. It is invoked to close debate, protect institutions, and frame critics as either self-interested or anti-science.

That is the real problem. When best practice science becomes a phrase that ends the conversation instead of opening it, it stops functioning as science and starts functioning as authority.

And if the outcomes of this so-called best practice science are fewer working fishers, less domestic seafood production, more import dependence, higher barriers to entry, and steadily shrinking sovereign food capacity, then Australians are entitled to ask a simple question.

Best practice for whom?

The phrase that ends the conversation

One of the most powerful features of the phrase best practice science is that it sounds morally settled before any actual discussion begins. It implies that the serious work has already been done, the technical questions have already been resolved, and anyone still objecting is either uninformed, emotional, or protecting their own interests.

That is a very convenient position for institutions to occupy.

Because once best practice science becomes the default language of justification, the debate subtly changes. The focus shifts away from outcomes and onto credentials. Instead of asking whether a decision actually improved the system, we are pushed to ask whether the process looked scientific enough on paper. Instead of asking whether domestic production was unnecessarily damaged, whether effort displacement was properly accounted for, or whether local knowledge was ignored, we are invited to admire the complexity of the model, the thickness of the report, and the prestige of the institution behind it.

This matters because process is not the same thing as truth.

A decision can be wrapped in technical language, peer review, modelling, workshops, consultation summaries, and precautionary framing, and still be deeply flawed in practice. It can still rest on weak assumptions. It can still misunderstand how fisheries actually operate on the water. It can still confuse reduced landings with reduced abundance. It can still ignore effort compression, weather disruption, market distortion, area closures, quota settings, cost pressure, and policy-driven changes in fisher behaviour.

None of that disappears because someone says the words best practice science.

Science is not the same as the system built around it

A big part of the problem is that commercial fishers are not simply dealing with science. They are dealing with an entire institutional system built around scientific language.

There is a difference.

Science, in the true sense, is about observation, hypothesis, testing, uncertainty, revision, and intellectual honesty. But what fishers often encounter is a much larger stack of institutions and incentives that sits around the science and speaks in its name. That stack includes data collection systems, stock assessment models, management frameworks, consultation processes, risk settings, enforcement systems, communications teams, regulatory departments, funded research bodies, ministerial expectations, public relations pressures, and campaign narratives from outside groups.

By the time a fisher is told a decision is based on best practice science, they are rarely looking at raw science alone. They are looking at science filtered through bureaucracy, politics, risk aversion, institutional self-preservation, and administrative convenience.

That does not mean all of it is false. It means it is not neutral.

And that distinction matters. Because when this whole stack is presented to the public as though it is simply “the science”, institutions gain a kind of borrowed authority. Policy choices are recast as technical necessities. Judgement calls are presented as objective outcomes. Value trade-offs disappear behind equations. Real uncertainty is softened into official confidence.

The result is not greater transparency. It is a cleaner-looking version of opacity.

Who benefits from best practice science?

This is the question that is often treated as impolite, but it is the question that most needs asking.

Who benefits when best practice science becomes the dominant justification for major decisions?

Agencies benefit, because technical language strengthens their legitimacy and insulates them from criticism.

Research bodies and consultants benefit, because expanding layers of modelling, reporting and review create ongoing demand for specialised expertise.

NGOs benefit, because scientific language gives campaigns moral authority and makes policy goals look inevitable rather than contested.

Large operators often benefit more easily than small ones, because they are better placed to absorb compliance burdens, shifting rules, paperwork costs, consultation overhead, and commercial uncertainty.

Institutions as a whole benefit because complexity itself becomes a source of power. The harder the framework is for ordinary people to interrogate, the easier it is to present it as something that must simply be trusted.

Now compare that with who often wears the cost.

Small working fishers wear the cost. Regional communities wear the cost. Consumers wear the cost. Local processors wear the cost. Young people trying to enter the industry wear the cost. Australia’s domestic food resilience wears the cost.

This is where the issue of scale bias becomes unavoidable. A system can look neutral on paper while producing uneven effects in practice. The reporting burden may be the same. The compliance rule may be the same. The closure may be applied evenly. The model may treat everyone as a datapoint. But the real world does not absorb those costs evenly.

The larger player has more buffer. More staff. More legal help. More access to capital. More capacity to absorb downtime. More ability to spread fixed costs across volume.

The smaller operator does not.

So when best practice science is embedded inside a system that ignores scale, it does not simply manage risk. It accelerates consolidation.

When failure leads to more process

In a healthy scientific culture, poor outcomes should trigger humility. If the model did not match reality, if the intervention did not produce the promised result, or if the costs turned out to be far greater than expected, the framework should be opened up and reassessed.

But that is not usually what happens.

In practice, when the outcomes of best practice science do not line up with the promise, the answer is often not less confidence in the framework. It is more of the framework.

More modelling. More reporting. More restrictions. More workshops. More consultation. More calls for better data. More burden shifted onto the productive sector. More layers inserted between lived experience and decision-making.

Failure rarely discredits the architecture. It just expands it.

This is one of the clearest signs that something deeper is going on. A genuinely outcome-based system would ask whether the assumptions were wrong, whether the data series missed major changes, whether landings were being misread, whether policy settings distorted effort, whether closures changed the operating footprint, whether market conditions altered targeting behaviour, and whether local knowledge should have been weighted more seriously.

Instead, the productive base is too often told that if the system is not delivering, the answer is even more compliance, even more data extraction, and even more patience.

That is not scientific humility. That is institutional self-protection.

A model is not reality

This point needs to be said plainly. A model is not reality. A dataset is not reality. A management summary is not reality. And a phrase like best practice science is certainly not reality.

Reality is what happens on the water, in the market, at the co-op, in the processor, in the fuel bill, in the weather window, in the closure map, and in the final decision a working fisher makes about whether they can keep going another season.

Reality is whether fishers are leaving the industry.

Reality is whether domestic seafood production is shrinking.

Reality is whether Australians are eating more imported product while local operators are pushed out in the name of sustainability.

Reality is whether policy settings are preserving a sovereign food-producing base or slowly dismantling it.

If those outcomes are moving in the wrong direction, then the institutions invoking best practice science should not be offended by scrutiny. They should welcome it. Because science worth respecting does not fear accountability. It depends on it.

What would real best practice science look like?

If we are going to use the phrase best practice science, then it should be held to a far higher standard than it currently is.

Real best practice science would publish uncertainty honestly, not bury it in technical language.

Real best practice science would distinguish between landings, effort, access, targeting behaviour, and abundance instead of blurring them into a convenient management story.

Real best practice science would test models against lived industry experience rather than treating working fishers as politically inconvenient anecdotes.

Real best practice science would acknowledge when closures, quota settings, weather disruption, flood events, market incentives, or participation decline have reshaped the data.

Real best practice science would make its assumptions transparent enough for the governed to interrogate, not just the governing class.

Real best practice science would be judged by outcomes as well as method. Did it improve stock understanding? Did it avoid unnecessary damage to domestic production? Did it preserve resilience? Did it reduce uncertainty honestly? Did it support the long-term public interest?

And perhaps most importantly, real best practice science would not treat criticism as heresy. It would recognise that challenge is part of the process, especially from the people whose lives are most directly affected by the decisions.

The public cost of getting this wrong

Some people will read an argument like this and assume it is only about commercial fishers pushing back against uncomfortable regulation. That is too narrow, and frankly too convenient.

The cost of misusing best practice science is not confined to fishers.

The cost shows up in the hollowing out of domestic production.

It shows up in the loss of regional capability.

It shows up in fewer pathways for young entrants.

It shows up in rising dependence on imported seafood.

It shows up in the collapse of trust between institutions and the productive base.

It shows up in a country surrounded by water becoming less capable of feeding itself from its own coastline.

That is not a narrow industry issue. That is a national resilience issue.

If Australia continues to confuse institutional process with real-world performance, then the phrase best practice science will become part of the problem rather than part of the solution. It will become a label used to sanctify decline, rationalise consolidation, and shield decision-makers from the consequences of their own frameworks.

At that point, the public should stop asking whether the language sounds impressive and start asking whether the outcomes are acceptable.

Because best practice science should not be judged by how often it is invoked.

It should be judged by what it actually delivers.

And if it cannot survive that test, then perhaps it is not best practice science at all.

Perhaps it is just best practice spin.